01.2023

JET

International Journal:

Emerging Technologies in Learning
PapersInfluences of Problem-Based Online Learning on the Learning Outcomes of Learners
Predictable Factors that Help Students Engage in Online EFL Classroom and their Rela- EEG Spectral Feature Markers as an Indicator of Human Cognitive Processtionship to Self-Management
Enhancement of Online Education in EngiThe Importance of Using the Internet ofneering College Based on Mobile WirelessThings in Education
Communication Networks and IOT
A New Resource Recommendation Method
for Experiential Leaching Based on the Com- A Career Recommendation Method for College Students Based on Occupational Values
pletion Degree of Online Learning Tasks
The Eﬀect of a Proposed Strategy according Prediction of Students Performance Levelto the Design Thinking Model in Mathemat- Using Integrated Approach of ML Algorithms
The Degree of Practicing Creative Thinking
ics Achievement and Personal Intelligence
Skills by Basic School Teachers in Emirate ofamong Students of Sixth-Class Scientific
Sharjah from their Point of View
The Implementation of Online Learning in
An Analysis of the Categories and StrucConventional Higher Education Institutions
During the Spread of COVID-19: A Compara- tures of Expertise for Students’ Cognitive
Statestive Study
An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment
Strategies
Development of the Hands-free AI Speaker
System Supporting Hands-on Science Laboratory Class: A Rapid Prototyping
Enhancement the Educational Technology
by Using 5G Networks

Short Papers
Conceptual Statistical Assessment Using
JSXGraph
Analysis of STACK Answer Data Using PenStroke Data from a Calculation Notebook
and Item Response Theory

Table of Contents—iJET Vol. 18, No. 01, 2023

Table of Contents
Papers
Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship to
Self-Management ............................................................................................................................................. 4(Abdelhameed Al Awabdeh, Abeer Hameed Albashtawi)
The Importance of Using the Internet of Things in Education ....................................................................... 19(Suaad Hadi Hassan Al-Taai, Huda Abbas Kanber, Waleed Abood Mohammed al-Dulaimi)
A New Resource Recommendation Method for Experiential Leaching Based on the
Completion Degree of Online Learning Tasks ............................................................................................... 40(Ligang Jia)
The Effect of a Proposed Strategy according to the Design Thinking Model in
Mathematics Achievement and Personal Intelligence among Students of Sixth-Class Scientific ................. 55(Areej Khuder Hassan)
The Implementation of Online Learning in Conventional Higher Education Institutions During
the Spread of COVID-19: A Comparative Study ........................................................................................... 68(Mohd. Elmagzoub Eltahir, Najeh Rajeh Alsalhi, Geraldine Torrisi-Steele, Sami Sulieman Al-Qatawneh)
An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies ........... 100(Suyun Wen)
Development of the Hands-free AI Speaker System Supporting Hands-on Science Laboratory Class:
A Rapid Prototyping .................................................................................................................................... 115(Gyeong-Geon Lee, Minji Choi, Taesoo An, Seonyeong Mun, Hun-Gi Hong)
Enhancement the Educational Technology by Using 5G Networks ............................................................ 137(Duha Khalid Abdul-Rahman Al-Malah, Ban Hassan Majeed, Haider TH.Salim ALRikabi)
Influences of Problem-Based Online Learning on the Learning Outcomes of Learners ............................. 152(Chi Zhang)
EEG Spectral Feature Markers as an Indicator of Human Cognitive Process ............................................. 164(Juris Porozovs, Viktors Veliks, Aija Kļaviņa, Gunita Praulīte)
Enhancement of Online Education in Engineering College Based on Mobile WirelessCommunication Networks and IOT ............................................................................................................. 176(Jaafar Qassim Kadhim, Ibtisam A. Aljazaery, Haider TH.Salim ALRikabi)
A Career Recommendation Method for College Students Based on Occupational Values ......................... 201(Lei Wang, Yuanyuan Fu, Yingchao Zhang)
Prediction of Students Performance Level Using Integrated Approach of ML Algorithms ....................... 216(Mirza Azam Baig, Sarmad Ahmed Shaikh, Kamlesh Kumar Khatri, Muneer Ahmed Shaikh,
Muhammad Zohaib Khan, Mahira Abdul Rauf)
The Degree of Practicing Creative Thinking Skills by Basic School Teachers in
Emirate of Sharjah from their Point of View ............................................................................................... 235(Manal Mahmoud Al-Shaikh, Najeh Rajeh Alsalhi)
An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States ........................ 254(Mingzhe Niu)

Short Papers
Conceptual Statistical Assessment Using JSXGraph ................................................................................... 269(Curtis Hooper, Ian Jones)
Analysis of STACK Answer Data Using Pen-Stroke Data from a Calculation Notebook and
Item Response Theory .................................................................................................................................. 279(Yasuyuki Nakamura, Kazusa Fujimoto, Kako Ito)iJET ‒ Vol. 18, No. 01, 2023

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

Predictable Factors that Help Students Engage
in Online EFL Classroom and their Relationshipto Self-Management
https://doi.org/10.3991/ijet.v18i01.35257

Abdelhameed Al Awabdeh1, Abeer Hameed Albashtawi2()English Department & Literature, University of Jordan, Aqaba, Jordan
2
Luminus Technical University College, Irbid, Jordan
abeerbashtawi11@gmail.com

1

Abstract—Online teaching has become a decisive factor to keep growing
in higher education settings. The present analytical correlational study examined the predictable factors that would help EFL Jordanian students engage in
online learning in English courses after COVID-19 pandemic. To achieve theobjective of the study, a validated and a reliable survey was administered toinvestigate the students’ perceptions toward various factors that would affecttheir success and engagement in online EFL classroom. Additionally, therelationship among some factors was investigated. Fifty-nine EFL students in
an English Language Skills course in a technical university college in Jordan
were randomly selected to respond to a questionnaire which was adapted from
Bernard [7]. Data of the study were analyzed using the descriptive statistics(means and standard deviations) and Person Correlation Coefficient. Findingsof the study confirmed that relationship among some factors has uncovered theimportance of having basic skills in building other skills. A positive relationshipwas also found between the factors that help students engage in online learning and self-management. Findings of the study would be helpful for teachers,
instructors, and course designers when designing and delivering online courses
as well as policy makers.
Keywords—EFL, interaction, online learning, self-confidence,
self-management

1

Introduction

Unprecedented rapid global changes in the educational systems have occurred as a
response to COVID-19 where the World Health Organization declared it as a pandemic.
COVID-19 has caused the change of the educational systems. It has forced all the
educational institutions in most countries to shift to remote learning in all its different
kinds whether synchronous or asynchronous. It has been affirmed by the United Nations
Educational, Scientific and Cultural Organization (2020) that 1,186,127,211 learners
were affected across the world, approximately about 67.7% of the total enrolled learners from 144 country-wide closures [3].

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

The rapid migration from conventional learning to online has raised many questions
regarding the preparedness of the educational institutions generally and uncovered theimportance of utilizing technology in various sectors. Before COVID-19, the utilization of technology was not clear enough to the extent that enables these institutions
deploy the emergent technologies appropriately. However, it has been noted that thenegative effects of this pandemic on people all around the world have increased thevalue of investing in technology as a sustainable method of teaching. This pandemic
has opened new doors to several institutions. Thus, all policy makers and educators
have started thinking that migrating to online education is not a temporary solution andit should be looked at as the most sustainable method of learning. Meanwhile instructional activities should become more blended considering the challenges experienced
during the pandemic and transform them to opportunities, people should be preparedpositively toward adapting to the use of technology as an essential partner in the process of learning [2].
Online courses delivery has imposed big challenges on both teachers and students.
Teachers need intensive training that enable them to shift successfully to online environments. Similarly, students are not familiar to this new emergent kind of education
particularly in the Arab world. Online learning necessitates students to put great efforts
in the learning process because they have to be independent learners. Thus, meetings
the challenges together could help both teachers and learners discover the factors thatmake the learning process flexible, understandable, effective, beneficial and feasible.
During the pandemic, many universities around the world have fully digitalized
their operations understanding the dire need to adapt according to the current situation.
There was an overnight shift of conventional classrooms into e-classrooms, that is, educators have shifted their entire pedagogical approach to tackle new market conditions
and adapt to the changing situations. During COVID-19 tough time, the concern was
not about whether online teaching–learning methods could provide quality education,
it was rather how academic institutions would be able to adopt online learning in sucha massive manner [11]. Examining students’ perceptions would undoubtedly reveal the
areas that need improvement in online teaching as well as would give insights to bothteachers and educators about the opportunities that can be invested [29].
Investigating the data that was collected from students could provide in-depth understanding of the effectiveness of online learning [28]. It has been stated by Jackson andHelms [16] that, “the learner’s or student’s perception offers crucial information inassessing and defining quality” (p. 8). During the rapid transition to online teaching asa result of COVID-19, the need to address the factors that would improve the students’performance in online courses from their perspective has become an urgent need. This
study intended to explore the factors that constitute students’ perceptions about the decisive factors that would help in students’ engagement and success in EFL online courses.
Several factors were considered in the present study after reviewing the past studies. These factors included students’ pre-requisite skills, general beliefs about online
learning, desire of interaction, and self -management, as confirmed by Bernard andothers [7]. Similarly, Jiang and Ting [17] affirmed that the importance of instructional
emphasis on learning through interaction significantly influenced students’ perceptionsof learning. Moreover, flexibility and availability of opportunities to communicate
teachers in online environment were among the factors that affected students’ positively[18]. Further, technology is a crucial factor that could play a vital role in the success of

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

online learning [13], [14]. Students’ ability to overcome the technical problems and theway they deal with them have been valued from the students’ perspectives’ [27].
This study was carried out to present the factors that would help the students engage
and succeed in online learning from their perspective in a not the Technical University
College in Jordan during the COVID-19 pandemic. Further, it intended to examine therelationship between these factors and self-management. By the end of March 2020,
Luminus Technical University College (LTUC) has transmitted to online teaching as aresult of COVID-19. LTUC has distinguished itself from all other Jordanian institutionsin its preparedness to meet the pandemic. Thus, its transition was very smooth due tothe LMS it has and the quality of the training its staff has received. This study focuseson the following research questions:
1. What are the most predictable factors that help students engage in online learning in
EFL classroom?
2. What is the level of self-management for students engagement in online learning in
EFL classroom?
3. Is there any significant statistical relationship between the students’ responses on
predictable factors that help students engage in online learning and their responses
on self-management?
The results of this study would definitely provide implications for the improvementof teaching online English courses. In particular, it is hoped that this study would provide a better understanding of the issues related to teaching and learning in online in
the EFL context. Additionally, it would provide the teachers with some of the practices
that help students to be more self-disciplined.

2

Literature review

Online learning literature has expanded considerably after the appearance of
COVID-19 in 2020. Online learning emphasizes Internet-based courses offered synchronously and asynchronously. Synchronous learning is a form of learning with directinteractions between students and teachers while simultaneously using online forms
such as conferences and online chat. Meanwhile, asynchronous learning is a form oflearning indirectly (not at the same time) using an independent learning approach. Some
subject matter is designed and displayed on LMS on Moodle, or email systems, blogs,
online discussions, Wikipedia, videos, articles, and other platforms [26], [23]. During
the shift to online learning, public and private institutions have faced many obstacles.
These obstacles represented firstly in the poverty of the infrastructure. Secondly, theshift from teaching traditionally through face-to-face to more online methods. Thirdly,
the lack of training the teachers have received in changing the instructional materials toonline and the methods to interact with students and reach each other easily. Fourthly,teachers have not been informed even theoretically about the difference between teaching face-to face and online. Fifthly, the difficulties in dealing with technology.
Reviewing the factors that help in students’ engagement in online learning in the paststudies while considering the challenges that face students, is a very important issue needs
to be addressed. Thus, this study presents the issue from the students’ perceptions. Several
factors have been considered while investigating this issue. Students’ digital skills,

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

beliefs toward online learning, self-management, and interaction [7]. Further, in Parkes& Reading [22], students were also found to be poorly prepared for several e-learningcompetencies and academic-type competencies. Additionally, there is a low-level preparedness among the students concerning the usage of Learning Management Systems.
The unexpected change to online learning has become a measure of organizational
agility for several academic institutions which have primarily focused on the transferof the educational content to the digital world and not specifically on online teaching and delivery methods [1]. Nonetheless, it was a reminder of the lack of resourcesin academic institutions and the social marginalization of students, where insufficient
access and availability of the internet and the lack of latest technology have affectedorganizational responsiveness and students’ capacity to participate in digital learning.
Lack of proper interaction with instructors is another major concern associated with
online learning [31]. Additionally, virtual classes cannot be of interest to students who
are tactile learners. Conventional classroom socialization is another major missing in
online learning. Students only communicate with their fellows digitally and never see
fellow students in person, and thus the real-time sharing of ideas, knowledge and information is partially missing from the digital learning world.
Twigg [25] linked the problems of online education to: (a) the requirement of
separate quality assurance standards, (b) programs having low (or no) quality standards,and (c) the lack of consensus on what constitutes learning quality. Federal regulators,
accreditors, state regulators, administrators, faculty, and students need to have a better
understanding of what contributes to quality in online learning [21].
Eaton [12] argued that online education has “the potential to disrupt basic quality
expectations within the academic community throwing higher education and accreditation into disarray” (p. 10). Recognizing Eaton’s and others concerns, Yang andDurrington [30] stated that “To prevent disruption and chaos in the education arena,accrediting organizations need to assess distance learning over the Internet to verify
that it meets their standards thereby establishing the credibility of the medium to otherstakeholders” (p. 2). One step in this direction is to more specifically identify problems
and issues related to online education [30].
From a macro viewpoint, very little is established regarding the decisive predictorsthat affect online education [20]. Furthermore, the capacity to successfully teach digitally is likely to differ based on the wide range of learning goals that guide the instructional and the educational priorities [19]. Consequently, educators and policymakers
alike recognize the importance of understanding their students’ perceptions in online
learning environments as they play a vital role in uncovering the factors that affectthe learning process. Consumer demands, organizational excellence and efficiency, andaccountability to stakeholders should be the driving forces that ensure the quality of
online courses.

3

Past studies

Past studies [5, 6, 15, 24, 4, 8] have reported different favourable and unfavourable
perceptions by students on online learning. Various studies have reported that having
digital literacy skills in online environment, the general beliefs toward online learning,

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

self-management, and interaction are among the most important factors that affectonline learning from students’ perspectives.
Research has affirmed that students have had some technical difficulties in online
learning. In a Jordanian study, Almahasees et al., [5] confirmed that although the meritsof online learning which have been represented in self-learning or directing, flexibility,
and reducing the costs, teachers and students have uncovered some problems related totechnical and internet issues, lack of interaction and motivation, data privacy and security.
In another two recent studies conducted by Al-Nofaie [6] & Al-Qahtani [15] within
the Saudi context, students showed positive attitudes toward online learning. In her
study, Al-Nofae [6] identified the challenges of online learning from students’ perspectives on their learning experiences during the sudden shift to online learning andsuggested practical solutions. 25 university students majoring in the English language
and enrolled in Morphology class were included in the study. Based on an analysis of
the students’ learning logs, the results showed students’ preference to asynchronous
environment rather than synchronous due to its flexibility. Further, the study revealed
that virtual education was not always preferred to students. Similarly, Al-Qahtani [15]investigated teachers’ and students’ perceptions of English as a foreign language (EFL)in virtual classes. The study aimed to highlight the effect of this kind of virtual class onenhancing communication skills. This study was conducted in the English Department,
King Khalid University. 30 teachers and students who were divided into two equalgroups were included in the study. Data were collected via a questionnaire. The results
showed positive attitudes toward teaching and learning through EFL virtual classes
from the students and the teachers’ perspectives. The results affirmed the significant
role of virtual courses in enhancing communication skills.
Further, Ta’amneh [24] examined students’ attitudes towards virtual classes in learning English courses. He aimed to identify the obstacles that face students in online
learning. The sample of the study consisted of 336 students at Taibah University in
Saudi Arabia. A questionnaire was administered to collect data. The results asserted
that students’ attitudes towards the use of the online learning were positive. To elaborate more, students considered virtual learning as a tool that helped them in organizing
their homework, assignments, and time. They agreed that the use of virtual learning
saved their times and efforts. Furthermore, students confirmed the existence of somepedagogical, technical and personnel problems while attending virtual classes.
In Alshumaimeri & Alhumud [4] study, students affirmed the effectiveness of virtual
classrooms in enhancing their communication skills. The sample consisted of 43 female
students from the English Department at a Saudi university. A mixed method design
was implemented to collect data. Two data collection instruments were used: a questionnaire and observations. The findings showed students’ lack of confidence, anxiety
when making mistakes, and lack of vocabulary as their greatest challenges when communicating in English. The qualitative data revealed that virtual classrooms could playa significant role in enhancing students’ communication skills. However, despite thepositive attitudes toward online classes, students agreed that the lack of face-to-facecommunication was a major obstacle in online learning.
In another EFL context, Cakrawati [8] investigated students’ perceptions on the useof online learning platform in English as a Foreign Language (EFL) classroom. 40 participants were involved in the study. Quantitative and qualitative data were collected via

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

a questionnaire and interviews. Most of the participants affirmed the efficacy of using
Edmodo or Quipper in English teaching and learning in terms of time, online platforms
could be a good choice to encourage practicing language skills, acquiring new vocabulary, and helping students in understanding the content. However, the results showedthat slow-speed internet was one of the main difficulties in using online.
Some of the studies have focused on the instructional practices in online environment between the students and their teachers, the students and the content, and among
the students themselves. For example, Cheung, A. [9] has investigated the interaction
patterns that occur in online environment and its effectiveness on the students. In his
study, Cheung examined multi-modal exchanges between the teacher and the students.
Data were obtained via 80 recordings from whole-class and small-group sessions overin four months. Various modes of synchronous computer-mediated communicationthat the teacher utilized were analysed. The findings showed that skilful students could
demonstrate remarkable interactional skills during small-group sessions. One concern emerged through interaction was students’ reticence, though it was alleviated byextending the wait-time. The study confirmed the importance for re-conceptualizing theconstituents of classroom interactional practices. Similarly, Jiang and Ting [17] foundthat the degree of instructional emphasis on learning through interaction significantly
influenced students’ perceptions of online learning. Results indicated that percent ofgrade weight on discussion and instructor’s specification of requirements of students’contributions in discussion were significantly and positively correlated to students’ perceived amount of learning.
Reviewing the past studies, it was found that very few studies have considered all
the elements that might affect the students’ engagement in online education in the Arab
context. The researchers in the presents study tried to highlight the most important factors that might contribute to a better online education in the Jordanian context in particular and the Arab world in general. Because of the dearth of studies in online educationin Jordan, the present study would contribute to the literature of online education not
only in Jordan but also in the Arab context.

4

Methodology

4.1

Participants and context

The present analytical correlational study attempts to explore the predictable factors that help students’ engage in online English courses and their relationship to
self-management for students at Luminus Technical University College (LTUC), in
Jordan. Researchers who choose this design are interested in research that aims todetect the relationships between some variables [10]. The target population in thisstudy is represented by all students who registered for the English Language Skills
course at the Foundation Department, LTUC, Jordan. The sample included 59 students
who were randomly selected. The included students in the study have already passed
some other online English courses. The English Language Skills course was delivered
online through D2L platform integrated with Zoom. The students’ ages ranged between
19–21 years old. They were also homogenous in terms of their mother tongue (which isiJET ‒ Vol. 18, No. 01, 2023

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Arabic), cultural background and the years of studying EFL (12 years) in the Jordanianschools.
4.2

Data collection

Data were collected using a questionnaire for predicting online learning achievement
which was adapted from Bernard et al., [7]. The questionnaire used in this study consisted of three parts. The first part was constructed to collect background information
about the participants (age, gender, class level, and number of English online hours thathave been taken).The second part of the questionnaire includes 18 items, which covered
three domains (Confidence in prerequisite skills, (5) items, General beliefs about online
learning, (7) items, and Desire for interaction, (6) items). The third part includes 4 items
on self-management. Five-point Likert scale (1=never, 2=occasionally, 3=sometimes,
4=usually and 5=always) was used to obtain data on students’ perceptions toward thefactors that help them engage in online English courses and self-management. Validation of the instrument was conducted by asking a jury of university instructors to give
their remarks on the validity of the questionnaire. Analysis of reliability of the questionnaire refers to how the items in the questionnaire are interrelated among themselves.
For questionnaires, one of the most popular methods is the Alfa Cronbach method.
Value of the Alfa Cronbach method results of 59 students were (0.82–0.89) for thedomains and (0.90) for the whole questionnaire
4.3

Data analysis

The factors that help students engage in online learning in EFL classroom wereaddressed through analysing students’ responses to 18 items. The researchers examined these factors in relation to students’ self-management. Self-management consistedof 4 items. To specifically address this, SPSS software was considered to answer
the research questions. The data were analyzed by using the descriptive statistics(frequencies, percentages, means, and standard deviation) and Pearson Correlation
Coefficient.

5

Results
The results of the present study are presented by research question:

Question 1: What are the most predictable factors that help students engage in
online learning in EFL classroom?
As presented in Table 1, students’ responses on the most predictable factors that
help them engage in online learning in EFL classroom were computed. Table 1 showsthat “confidence in prerequisite skills” domain ranked first based on the mean value(4.13±0.74). Meanwhile, “desire for interaction” domain ranked second based on
the mean value (3.95±0.76), “general beliefs about online learning” domain ranked theleast based on the mean value (3.35±0.57), which is considered as neither high nor low.
The results indicate that overall mean score of the students’ responses on predictablefactors that help them engage in online learning in EFL classroom was (3.77±0.49).

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Table 1. Means and standard deviations of the students’ responses on the most predictablefactors that help them engage in online learning in EFL classroom# of Items

Mean

Std. Dev.

Degree1#

Confidence in prerequisite skills

The Domains

5

4.13

0.74

High

3

Desire for interaction

7

3.95

0.76

High

2

General beliefs about online learning

6

3.35

0.57

Mid

18

3.77

0.49

High

The Whole Predictable Factors
Note: Out of (5).

As presented in Tables 2, 3, and 4, the items in each domain were arranged based on
the mean value of each item.
Table 2 presents the first four top items which included, “I am able to easily accessthe online classes via Zoom” and it ranked the first based on the mean value (4.56±0.82),“I have been trained by my power users in the college to communicate electronically
via LMS and Pulse application which has been downloaded on my mobile” and it
ranked the second based on the mean value (4.39±1.00), “I am able to communicate
with my classmates as well as my teachers electronically” and it ranked third based on
the mean value (4.25±1.03), and “I can text my teachers and my classmates via LMS on
my mobile” ranked the forth based on the mean value (4.27±1.08), where they all considered as high. However, item number 5, “I feel comfortable communicating online
in English” ranked the least based on the mean value (3.29±1.44), which is considered
as neither high nor low. Based on the overall mean score of the students’ responses on
confidence in prerequisite skills domain it was (4.13±0.74).
Table 2. Means and standard deviations of the students’ responses
on confidence in prerequisite skills items
Mean

Std. Dev.

Degree1#

I am able to easily access the online classes via Zoom.

The Items

4.56

0.82

V. High2

I have been trained by my power users in the college tocommunicate electronically via LMS and Pulse application which
has been downloaded on my mobile.

4.39

1.00

V. High3

I am able to communicate with my classmates as well as myteachers electronically.

4.25

1.03

V. High4

I can text my teachers and my classmates via LMS on my mobile.

4.27

1.08

V. High5

I feel comfortable communicating online in English.

3.29

1.44

Mid

4.13

0.74

High

The Whole Domain
Note: Out of (5).

Table 3 shows that item 9, “I can practice English grammar via the online activities
outside the class” ranked first based on the mean value (4.34±0.86), which is considered as very high. However, item number 8, “Learning in class or online at home is thesame” ranked the least based on the mean value, which is considered as low (2.00±1.25).
Based on the analysis of the results, items 7 and 11 where considered as neither high nor low
based on the mean value (3.10±1.31) and (2.66±1.65) respectively. The total mean score of
the students’ responses on general beliefs about online learning domain was (3.35±0.57).

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Table 3. Means and standard deviations of the students’ responses
on general beliefs about online learning items#

The Items

Mean

Std. Dev.

Degree9

I can practice English grammar via the online activities outside theclass.

4.34

0.86

V. High10

I feel can improve my listening skills using the online activities andlinks provided by the teacher.

4.32

0.80

V. High6

I am motivated by the content on my LMS outside the class.

3.81

1.21

High

12

I believe I can complete online course without any difficulty.

3.47

1.32

High

7

Learning activities contain clear, detailed instructions for students
better than a conventional class.

3.10

1.31

Mid

11

Online learning at home is more motivating to me than a face-toface class.

2.66

1.65

Mid

8

Learning in class or online at home is the same.

2.00

1.25

Low

3.35

0.57

Mid

The Whole Domain
Note: Out of (5).

Table 4 shows that item 18, “I can interact with the teacher via the posts he shareswith us via the LMS outside the class” ranked first based on the mean value (4.46±0.84),
which is considered as very high when compared to the other items. With reference tothe remaining items 17, 14, 16, 15, and 13, all were considered as high. However, item
number 13, “As a student, I enjoy working with other students via Breakout rooms”
ranked the least based on the mean value (3.49±1.43) although it was considered ashigh. The results indicate that the overall mean score of the students’ responses ondesire for interaction domain was (3.95±0.76) and considered as high.
Table 4. Means and standard deviations of the students’ responses
on desire for interaction items#

The Items

Mean

Std. Dev.

Degree18

I can interact with the teacher via the posts he shares withus via the LMS outside the class.

4.46

0.84

V. High17

I can collaborate with other students outside the class tofinish the tasks and the assignments.

3.98

1.15

High

14

I feel that face-to-face contact with my teacher is necessary
for learning to occur.

3.97

1.25

High

16

Online learning activities encourages me to interact with thecontent outside the class.

3.92

1.06

High

15

Online learning activities encourage me to interact withstudents outside the class.

3.73

1.14

High

13

As a student, I enjoy working with other students via
Breakout rooms.

3.49

1.43

High

3.95

0.76

High

The Whole Domain
Note: Out of (5).

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Question 2: What is the level of self-direction or management for students
engagement in online learning in EFL classroom?
To answer the second research question, the researcher used the descriptive statistics(means and standard deviations) of the students’ responses on the self-direction or management in the EFL online class. As shown in Table 5, meanwhile item (19) “When it
comes to learning and studying, I am a self-directed person” ranked first based on themean value (4.37±0.81), item (22), “In my studies, I set goals and have a high degreeof initiative” ranked second based on the mean value (4.20±0.92), and both are considered as very high. However, item (21), “I am able to manage my study time effectively
and easily complete assignments on time” ranked the least based on the mean value(3.69±0.97). The overall mean score of the students’ responses on self-direction items
was (4.02±0.73).
Table 5. Means and standard deviations of the students’ responses on self-direction
or management items that help them engage in online learning in EFL classroom#

The Items

Mean

Std. Dev.

Degree19

When it comes to learning and studying, I am a self-directed person.

4.37

0.81

V. High22

In my studies, I set goals and have a high degree of initiative.

4.20

0.92

V. High20

In my studies, I am self-disciplined and find it easy to put time forreading and doing all my homework.

3.81

1.06

High

21

I am able to manage my study time effectively and easily complete
assignments on time.

3.69

0.97

High

4.02

0.73

High

The Whole Self-Direction or Management Items
Note: Out of (5).

Question 3: Is there any significant statistical relationship between the students’
responses on predictable factors that help students engage in online learning andtheir responses on self-management?
The third research question intended to identify the relationship between the predictable factors that help students engage in online learning and their responses on
self-management. To achieve this, the researcher used Pearson correlation coefficient.
The results are presented in Table 6.
Table 6. Pearson correlation coefficients of the study subjects’ responses
on the self-management and desire for interaction
Predictable Factors that Help Students
Engage in Online Learning

Pearson Correlation

P-Value

Confidence in prerequisite skills

0.525

0.009*

General beliefs about online learning

0.618

0.003*

Desire for interaction

0.659

0.002*

The whole predictable factors

0.692

0.001*

Note: *Significant at (α ≤ 0.01).

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

Table 6 shows that there is a strong positive correlation coefficient between the study
subjects’ responses on the predictable factors that help students engage in online learning and their responses on self-management.

6

Discussion

In this study, we have examined the predictable factors that affect the online learningin the Jordanian EFL context. These factors included the general beliefs about online
learning, confidence in the prerequisite skills, interaction and self-management. A quantitative research design was used to address the three research questions of the study.
Data were analyzed using SPSS tests which included descriptive statistics (means,standard deviation, frequencies, and percentages) and Pearson Correlation Coefficient.
This study has revealed that the students’ responses on the most predictable factors that
affect Jordanian EFL students engage in online learning in the English Language Skills
course are arranged as follows: (1) confidence in prerequisite skills domain ranked first,(2) desire for interaction ranked second, (3) and the general beliefs about online learning domain ranked third. While the study revealed a strong positive correlation coefficient between the students’ responses on the predictable factors that affect the students’
engagement in online learning and their responses on self-management. These findings
can be attributed to the awareness of the students’ ability to use technology at LTUC.
LTUC has adopted the blended methods of teaching very early before COVID-19 andit was in a continuous work to invest appropriately in the educational technology. Thus,
the shift to online learning took place smoothly, fast, and professionally.
Talking about LTUC context, its students are familiar with the use of technology inlearning. Even though, technical problems would appear from time to time. The resultsof the present study showed the students’ positivity toward online learning as well as
comfort with some of the basic skills and components of online learning which could
help in determining their self-confidence as users of technology and online learners.
Examples on these skills included the ease in accessing online classes via Pulse application which is integrated with Zoom, the ability to communicate electronically with
their teachers as well as their classmates via the LMS, and the comfort in communicating in English. With reference to the findings of our study, Al-Qahtani [15] asserted
similar results with regard to the improvement in communication skills in the virtual
classes. However, the results of the study were opposite to some of the results reported
in Alshumaimeri & Alhumud [4], where students showed lack of self-confidence andanxiety when making mistakes.
In addition, interaction has been looked at as one of the key factors that leads to thesuccess of online learning. The results showed that instructor’s interaction with theirstudents had a positive impact on the students’ perceptions toward online learning.
Further, students’ satisfaction in online learning was high because students received
training to use LMS and Pulse application. Even though, lack of internet connection
could affect the levels interaction. Opposite to what has been reported in Al-mahasees
et al., [5] study, where they affirmed that one of the obstacles in the online learning
was the lack of interaction among the students themselves, the teachers and the students, or the students and the content. Similarly, this was asserted by Alshumaimeri &14

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

Alhumud [4]. Thus, all the previous types of interaction should be encouraged in online
classes.
Furthermore, the results revealed that students were aware to the difference between
online learning and conventional learning. The results also affirmed that online learning
was neither better nor worse than face-to-face learning. These results were consistent
with An-Nofae [6] study where the results showed that online education was not always
preferred to students. Additionally, students showed that asynchronous environment
was more preferred and motivating than synchronous. This might be attributed to theflexibility in choosing the suitable time to do their assignments.
Self-management is one of the most important factors that help the success of online
learning from the students’ perspective. Stating course goals and objectives at the
beginning of any course should be communicated clearly with the students in first orientation classes. Getting know what is expected from the students would help students
overcome their fears from the course and would reduce the levels of anxiety. Monitoring students’ progress would be helpful to keep them motivated. As a result, students’opportunities to become self-directed learners would improve. In the current study, thestudents showed high level of self-management. Further, the strong relationship thatwas revealed between the prerequisite skills, general beliefs toward online learning,and desire for interaction and self-management could draw the teachers’ attention to
the factors that could enhance the online learning process. Self-management would
definitely leads to creating an independent learner. Online learning needs students tobe more independent learners. The results of the study are supported by Ta’amneh [24]and Cakrawati [8], where they affirmed the goodness of virtual learning as a tool toimprove students’ ability in organizing their homework, assignments, and time during
the EFL online classes.
Establishing a healthy online environment is the teachers, course designers, andpolicy makers’ responsibility. Studies have been done to uncover the gaps in online
learning. Thus, generalizations cannot be made based on the present study. More studies should be conducted to examine different factors as well as various contexts need
to be considered to make online learning easy, and accessible to a greater audience ofstudents.

7

Conclusion

This analytical correlational study aimed at identifying the most predictable factors that help EFL Jordanian students engage in English online classes in an English
Language Skills course. This study also sought to identify the relationship between
the confidence in prerequisite skills of online learning, desire for interaction, and thegeneral beliefs about online learning and self-management. This study showed students positive perceptions toward the most predictable factors that help Jordanian
EFL students engage in online learning in an English Language Skills course. The
findings affirmed that confidence in prerequisite skills domain ranked first, desire forinteraction ranked second, and the general beliefs about online learning domain ranked
third. Further, the study confirmed a strong positive relationship between the students’iJET ‒ Vol. 18, No. 01, 2023

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Paper—Predictable Factors that Help Students Engage in Online EFL Classroom and their Relationship…

responses on the predictable factors that affect students’ engagement in online learningand their responses on self-management. The findings of the study could contribute to
the knowledge in the field of online learning in the EFL Jordanian context, and other
EFL contexts. Online learning is a complex process which necessitates various factors
to be considered to guarantee a successful and a sustainable online learning. Uncovering the relationship between different factors would help in the improvement of online
learning in various contexts.
Future studies may focus on other contexts. Different factors may be examined.
Further, employing different methods of qualitative and quantitative research methods
could contribute in the field as well. Meanwhile the present study has been limited to
studying confidence in the prerequisite skills, desire for interaction, the general beliefs
toward online learning, and self-management, other studies could consider the different
levels of interaction in online environment, the online preference learning style (e.g.,
synchronous or asynchronous) and social presence in online learning.

8

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[29] Vrasidas, C., & McIsaac, M. S. (1999). Factors influencing interaction in an online course.
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9

Authors

Abdelhameed Al Awabdeh (Ph.D) is an Assistant Professor at the Faculty ofForeign Languages, Department of English Language and Literature, the University of
Jordan, Jordan. His research interests are TESOL, applied linguistics, e-learning, andlanguage in use.
Abeer Hameed Albashtawi (Ph.D) is a senior lecturer at the Foundation andSupport Programs Department at Luminus Technical University College. Her major
research interests include applied linguistics, EFL/ ESL, EAP, psycholinguistics, reading and lexical development, motivation, and blended learning.
Article submitted 2022-09-10. Resubmitted 2022-10-25. Final acceptance 2022-10-25. Final version
published as submitted by the authors.

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Paper—The Importance of Using the Internet of Things in Education

The Importance of Using the Internet of Thingsin Education
https://doi.org/10.3991/ijet.v18i01.35999

Suaad Hadi Hassan Al-Taai(*), Huda Abbas Kanber,Waleed Abood Mohammed al-Dulaimi

Ibn Rushd For Human Sciences, University of Baghdad/College of Education, Baghdad, Iraq
suaad.hadi@ircoedu.uobaghdad.edu.iq

Abstract—The subject of the Internet of Things is very important, especiallyat present, which is why it has attracted the attention of researchers and scientists
due to its importance in human life. Through it, a person can do several things
easily, accurately, and in an organized manner. The research addressed important
topics, the most important of which are the concept of the Internet of Things,
the history of its emergence and development, the reasons for its interest andimportance, and its most prominent advantages and characteristics. The researchsheds light on the structure of the Internet of Things, its structural components,
and its most important components. The research dealt with the most important search engines in the Internet of Things, the steps of designing Internet ofthings systems. Among the important topics included in the research are the useof Internet of things technology in the educational field, the role of the Internet ofthings in solving education problems, and the most important applications of theInternet of things in education and its importance for students and teachers. Also
indicated the contribution of the Internet of Things to ensuring sustainable development, environmental protection and the most important areas of applicationof the Internet of Things. The research sample amounted to (152 teaching staff)
from Iraqi Universities and their academic achievement (84 Ph.D., 68 Master’s,
42 Bachelor’s) from (17) different scientific specializations; and (42 students).
The number of respondents who use the Internet in education is 191 and thenumber of those who do not use the Internet and teach in person is (3) from (194)person. Students and professors use smartphones in study and teaching at a rateof (97.42%) and computers and their necessities at a rate of (92.27%) are higher
than electronic devices and equipment including the smart board (11,86%). Useof IoT technology is of great and medium importance in providing electronic
lectures effectively, with a percentage of 96.91%, the use of it has a large andmedium role in solving education problems in giving lectures in light of healthcrises, especially in light of the Coronavirus crisis. Availability at the local andglobal levels, whether with the appropriate content or the selection of professors
anywhere in the world, at a rate of 98.45%.
Keywords—notifications, data, homes, cars, networks, platforms, systems,
technology

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Paper—The Importance of Using the Internet of Things in Education

1

Introduction

Internet of things technology has gained the attention of a large number of researchersin computer science and other disciplines, both scientific and human, due to its
importance in various fields of life, especially educational ones, due to the important
services it provides to most educational institutions [1, 2]. Given the importance of thesubject, the researchers chose to study the Internet of Things, clarify its applications,indicate its importance in education, and identify students and professors as the focus of
the educational process. The Research problem lies in the following two questions: What
are the applications of the Internet of Things in education; and What is the importanceof IoT technology for students and teachers? A statement of the concept of the Internet ofThings, its origins, and its importance. A statement of the applications of the Internetof Things in education. Explanation of the role of the Internet of Things in solving
education problems. Explain the importance of the Internet of Things for students andteachers. The questionnaire was distributed to a random sample of Iraqi universitiesstudents and professors through social media groups (WhatsApp, Telegram, and Viber).
In [3], the research paper seeks to clarify what is meant by the Internet of things andto indicate its characteristics and areas of application. And clarifying the justifications
that call for the need to benefit from the technology of the Internet of Things in the
educational field and the areas in which the Internet of things can be used to develop theservices and activities of educational institutions and clarifying the challenges facing
the use of the Internet of things in the field of education. The research came out of therecommendations, including increasing awareness of the importance of the role of theInternet of Things in developing the services of educational institutions, and allocating
many discussion panels and seminars on the subject of the Internet of things and its
services and benefiting from its advantages to solve educational problems. B. Walid
Youssef Mohamed Ibrahim and Rania Atef Shorb, Internet of Things Technology IoT:
Concept and Educational Applications (2). The research paper deals with the Internet ofthings in terms of the concept and statement of the main components and requirementsof the technology of the Internet of things from devices, protocols, middleware, applications, presentation, and distinctive characteristics of Internet of things technology
and its advantages and a statement of educational services within universities, including smart education and smart classes. C. Jamal bin Matar Al Salmi, Khaled Ateeq
Saeed Abdullah and Abdullah bin Salem Al Hinai, The Role of the Internet of Thingsin Knowledge Management in Information Institutions (3). This research paper seeks
to highlight the role of Internet of Things applications in supporting knowledge management activities in information organizations and thus improving their services. The
research relied on the descriptive approach, and through the investigation and analysisof the intellectual output published in the Arab world and abroad, to extrapolate theareas of the relationship between the Internet of Things and knowledge management
activities in information institutions. One of the recommendations of the research isthe need to take advantage of the Internet of things in information institutions to keep
pace with the technological changes that are taking place and to invest in the Internetof things applications for better service and to benefit from the huge data available
to them in making appropriate decisions. The advantage and benefits of the current
research paper compared to previous studies: The research paper was distinguished20

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Paper—The Importance of Using the Internet of Things in Educationfrom previous studies by showing the importance of using IoT technology in education
by taking the opinions of a sample of students and professors in Iraqi universities, aswell as stating the role of IoT in solving problems facing education. The benefit of previous studies was to write the theoretical framework for the research

2

The theoretical framework of the research

2.1

The concept of the Internet of Things

The Internet of Things means that all the devices and gadgets that we use in ourdaily lives can connect to the Internet and are managed through the mobile applicationof smartphones, through a computer, or through control devices that are also connectedto the World Wide Web. This means that these devices collect data, learn about user
decisions, are remotely managed, get updates, and rely on the Internet to communicate and function [4, 5]. In other words, Internet connectivity is no longer limited tosmartphones and computers in its narrow and traditional sense, but rather includes
televisions, surveillance cameras, house and room keys, home entertainment devices,
sports equipment, electronic panels, cars, etc. An IoT “thing” can be any object thathas the required computing power, Internet connectivity, and the ability to collect andtransmit data over a network without assistance or manual intervention. The technology embedded in the organisms helps to interact with the internal states or the external environment, which in turn affects the decisions taken as in Figure 1. [6, 7]. The
Internet of Things “is a modern technology that aims to connect all electronic devices
via the Internet so that they can communicate with each other through special protocols, and also communicate with humans by sending text messages. In this technology,
communication is a device with a device or a device with a human or A human with adevice, and often the human is the endpoint [8]. It is an emerging global Internet-based
information architecture whose purpose is to provide an information technology infrastructure to facilitate the safe and reliable exchange of goods and services. That is, its
function is to overcome the gap between things in the physical world and to represent
it in information systems [2, 9].

Fig. 1. Internet of Things concept

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Paper—The Importance of Using the Internet of Things in Education

It is a modern terminology that focuses on the future of the Internet, its uses, andadvanced applications based on the Internet. Kevin Ashton is the first to use the term
“internet of things” in 1999, and he is one of the pioneers in the field of technology. He
is the founder of the first research center at the Massachusetts Institute of Technology.
This term means that things will be able to be more useful with less effort by enablingthem to communicate with each other through the Internet [10–12]. Ashton expressed
IoT in his quote from the RFID Magazine article “If we had computers that knew everything using the data collected without human intervention we would be able to track
and account for everything and greatly reduce loss and cost” [13, 14]. The researchers
define the Internet of Things procedurally; it is a modern technology that connects
various devices and equipment to the Internet and facilitates the process of interaction
and communication between teachers and students to transmit data and informationelectronically over the network without manual intervention.
2.2

The emergence of the Internet of Things

The idea of the Internet of Things emerged as an initiative in 2000 by Kevin Ashton
to improve supply management by connecting RFID tracking data to the Internet, at his
workplace at Proctor & Gamble. While working there, Ashton had the idea of putting a
PFIC tag on lipstick and connecting it to a wireless receiver so he could monitor sales
and inventory and signal when additional storage was needed. He assumed that suchcollected data would help solve many problems in the real world. The term was thenused in theses by Neil Gere Shenfield, who was talking about similar ideas from the
Massachusetts Institute of Technology’s Media Lab in his book When Things Begin
to Think [15, 16]. In January of the same year, the Korean electronics company LG
announced the first refrigerator with the Internet of Things technology. In 2005, theInternational Telecommunication Union approved the research and development of theInternet of Things and published this in the annual report for the year 2005. In 2008,
the International Internet of Things Union, called IPSO, was formed to promote the useof the Internet of Things protocol on network-connected devices in energy consumption. By 2009, the concepts and applications of the Internet of Things dominated a largepart of the research and academic studies and the applications that tried to benefit fromit. There are more things connected to the Internet than the number of people connectedto the same network. In 2012, the sixth version of the Internet protocol was released,
which made it possible to assign a private address to everything on this earth without
restrictions or obstacles. Thus, we guarantee the possibility of connecting millions ofdevices [17]. The Internet of Things can operate through the use of smartphones, otherhandheld devices, generations of mobile data transmission services, as well as the use ofsoftware that relies on satellite systems or GPS remote sensors. Researchers in the fieldof the Internet of things have been able to develop tools, software, and the language ofcommunication via the Internet among themselves, which has led to today’s access towhat is known as the Internet of Things. Internet things are all tangible physical things(smart things) that are linked together over a network and can be identified on the Internet by sticking a clear and static IP address on the car, TV, Google goggles, and various
household items such as refrigerators, washing machines, alarms, home entrances, andhome appliances. Air conditioning, the goods, and products available on the shelves of22

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Paper—The Importance of Using the Internet of Things in Education

shops and on the animals on the farms, and everything we want to control, monitor, ordeal with through their electronic understanding through software and sensors that canconnect to the network. So these things can collect and share data. And the human being
in this case is the beneficiary of all these understandings and communications between
things that are communicated to each other over the Internet. It can even be monitored
and controlled online by a mobile smartphone app. All these things in airports, roads,
shops, hospitals, schools, universities, at home, and at work are under control and canbe managed and controlled via the Internet of Things by a mobile phone or any othermeans connected to the Internet [18, 19].
2.3

The importance of the Internet of Things

Interest in the Internet of Things, in general, is due to several reasons, including The
Internet of Things works by linking things together through a private identity. The applications of the Internet of Things are not very different for workers in information institutions who have previously dealt with RFID object tracking technology, as they are similarin tracking things through remotely connected sensors. However, the difference here isthat the communication between things and devices is via the Internet of Things throughthe Internet. The Internet of Things is an effective way to overcome some of the problems
facing the learner and helps him to overcome the barriers of time and space and enables
remote control of learning management successfully. The Internet of Things can help
strengthen the relationship between the student and the teacher so that each of them canreach each other through IoT applications without the need for an actual interview. The
teacher will be able to give an order to the textbook or the teaching aid to move towards theside where the student or students are in the classroom if the specialized robots that carryout the request are available. Every human being today expects to have a smartphone on
which he uses applications for multiple services. The school can, through its application,
provide the opportunity for learners and parents to communicate with the school via theInternet and use its electronic resources, attend classes, participate, express an opinion,and give commands to the devices available in the school to turn on or stop, or the like.
It can also be tracked and its data collected through the sensors installed on those objects.
Enterprises can use this technology to reduce production costs by increasing cooperation
and automation between machines instead of workers. Organizations can provide more
efficient, diverse, and real-time offerings based on real-time data collection from users.
Institutions must change their policy in advertising their goods to potential customers
and deliver those offers to the user at his doorstep through his smart mobile device at arecord and convenient time. The Internet of Things offers an effective means of effective
marketing its services through communication between its holdings. Rapid, sensitive,
and secure response to achieve the information needs of the beneficiaries within the Internet of Things network. Raising the productivity of devices and systems by increasing
the accuracy of data handling, remote implementation of operations, and less relianceon the human factor. Increasing the level of availability within information systems byimproving their ability to perform their work and the speed of access and obtaining information through those things. Enabling the delivery of things anytime and anywhere forsomeone who uses IoT network services perfectly. Develop reference services and allow
users to conduct reference questions and get answers from within the Internet of Things.

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Paper—The Importance of Using the Internet of Things in Education

Ensuring the users’ access within the Internet of Things network to view the resources
and information sources through their authenticated and defined identity, and sometimes
also through additional codes that are defined. Facilitate the process of saving and storing
resources through cloud computing and fog computing systems, through which information resources and sources are controlled, monitored, monitored, and controlled, and theirdata is accurately and permanently received. The ability to sense things and give appropriate alerts, warnings, and reports about them through its ability to process and analyze
data, and continuously monitor, track and make smart decisions [20–24].
2.4

Advantages of the Internet of Things

1. Data: The Internet of Things provides a lot of information that helps us to make theright decisions at the right time.
2. Time: IoT technology saves a lot of time by providing accurate information at theright time and in the fastest time.
3. Economic: IoT technology is very economical depending on the application
scenarios.
4. Tracking: IoT helps in tracking or monitoring various physical objects which in turnsaves time and money.
5. It contributes strongly to saving time, effort, and money by enabling the individual
and the organization to remotely control things to implement what is required of themaccurately, in addition to the possibility of understanding things among themselves
through sensors that communicate with each other via the Internet. This achieved
many results that contributed to saving time, effort, and money.
6. The human being is liberated from the constraints of time and place, where he canmanage and control things through the Internet protocol without the need for him to
be in the same place; and without his direct intervention in many cases, if he gives
instructions in advance. [24–26]
These are some of the services that can be provided by the IoT and are consideredamong its advantages:
1. Monitoring Manufacturing Equipment: Optimize processes with Industrial Internetof Things (IoT) technologies. Use advanced sensors and analytics to predict needed
maintenance and reduce unplanned downtime that cuts into production time.
2. Monitor customer equipment: New business models are designed that provide predictive maintenance and performance monitoring for the equipment they produce;
which provides a better customer experience.
3. Field Service Improvement: Access sensor data to improve field service schedulingensuring that the right technicians and tools are deployed before potential problems
become a major problem.
4. Optimizing the use of natural resources: IoT application of scenarios ranging
from energy to agriculture to deliver environmentally friendly energy transmission
and efficiencies at lower prices to customers.
5. Create safer cities: Connect better traffic management infrastructure, make emergency
systems more efficient, and reduce police and emergency medical response times.

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Paper—The Importance of Using the Internet of Things in Education

6. Creating Smart Buildings: Linking building devices and systems together to provide
more efficient operations and control capabilities for building owners, managers,
and occupants.
7. Improving field service: Improve service efficiency, from repairing broken streetlights to maintaining traffic lights to improving garbage truck lanes. [27]2.5

Characteristics of the Internet of Things

This system is characterized by a set of characteristics, the most important of whichare:
1. Interaction: About the Internet of Things, it can be anything that is interconnectedwith the global information and communication network infrastructure.
2. Heterogeneity: It is one of the main characteristics of the Internet of Things (IoT)
network based on its connection to different device systems and networks, where
they can interact with other devices or service platforms across different networks;
this supports the basic design requirements of the Internet of Things and its environments, in scalability and interoperability.
3. Dynamic Changes: The state of the connected devices within the Internet of Things
network changes from the state of connected or disconnected. Also, in the context
of devices including location and speed, addition, the number of devices can change
dynamically, the people connected to them, and the weight of the connection.
4. Things-Related Services: Able to provide things-related services within the constraints of things such as privacy protection and semantic consistency between
physical things and the virtual things associated with them.
5. Intelligence: The Internet of Things comes through a set of algorithms, software,and hardware that are linked to form an intelligent network whose capabilities areenhanced by surrounding intelligence. Which facilitates things through intelligent
response to a specific situation and supports it in carrying out specific tasks, including achieving interaction between the user and the device through standard saving
methods and a graphical user interface.
6. Sensing: The Internet of Things relies in its infrastructure on digital sensors andsensor applications that detect or measure any changes in the environment and givealerts and reports on their status, or even interact with the environment. Remotesensing techniques provide a means to create capabilities that reflect true awarenessof the physical world and the people in it.
7. Connectivity: The Internet of Things (IoT) enables interconnection through network accessibility and compatibility, the shared ability to consume and enhancedata production through collective intelligence, and provides intelligent communication between smart applications and humans.
8. Enormous Scale: The Internet of Things is characterized by massive capacity or
scale. As the number of devices within the IoT network that need to be managed
and that communicate with each other at least is greater than the devices connectedto the current Internet. In addition, the management of the data generated andinterpreted for application purposes is related to the semantics of the data, as wellas the efficient processing of the data.

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9. Safety: The Internet of Things (IoT) network is designed for the security of personal data and associated objects and to secure data transmission points within
networks from attacks and pollutants through an expansive security model.
10. Energy: It relies on energy efficiency and its devices do not require much energy,
to enable the Internet of Things network to operate with the least energy possible toensure its continuity in work and remote places.
11. Connectivity: The devices and sensor systems are connected through the Internet
or any other network.
12. Communication: Connect devices so you can communicate and analyze data.
13. Intelligence: It is the perception aspect provided by the Internet of Things devices,
through remote sensing and data collection that is analyzed.
14. Action: It is considered the most important step as it monitors and discusses thephenomenon based on previously collected data, as happens, for example, in temperature change decisions.
15. Ecosystem: Providing the appropriate environment for the presence of this technology, by providing the Internet for everything and choosing the appropriate platform.
16. User Interfaces: Smart objects can communicate with people appropriately, either
directly or indirectly.
17. Localization: Intelligent objects are aware of their physical location or can belocated, and mobile phone networks are suitable technologies to achieve them.
18. Embedded Information Processing: Smart objects have a processor or controller,
and in addition to storage capacity, these resources can be used to process andinterpret sensor information.
19. Identification: Objects are identifiable by a reader medium such as RFID or amobile device [28].
2.6

Using Internet of Things technology in the educational field

The Internet of Things improves education itself and contributes to the improvementof the physical and structural environment. A smart school has facilities that operatesmoothly to provide a higher level of personalized learning. And smart devices that areused in the educational institution use a Wi-Fi network to send data and receive instructions, which helps to create smarter lesson plans. Keeping important resources on track
and improving access to information, contribute to rapid communication between students and teachers in and out of the classroom anytime and anywhere. Therefore, manyeducational institutions have begun to realize the importance of introducing technology
and integrating it, especially the Internet of Things, in their daily educational methods.
There are some reasons and justifications for taking this step. The most important justifications for calling the use of the Internet of Things in education are as follows:
1. The education sector is always at the forefront of sectors that employ modern
technologies at its service, as this sector represents the main pillar upon which thetrends of the knowledge economy are built, which is the most prominent front for
major economies in the coming years.

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2. The technology of the Internet of Things and its applications in education has given
and will give many advantages and benefits to each teacher, student, and school,
and contribute to the tangible practical clarification of the educational process ina way that raises and raises the quality of education, and its outputs are what thecountry needs in terms of qualified human resources.
3. The applications of the Internet of Things in the field of education make the practical reality of educational institutions at all levels in line with everything new andmodern in the areas of technology applied in practice in education in many countries of the world.
4. Experts expect the Internet of Things to change how schools, universities, andteaching and learning institutions work, and bring about an anticipated revolution
in all stages of work, from teaching, guiding, learning, managing, following up,
and communicating among all members of the educational process to self-managed
customer services. Rather, it will be able to connect all parties to the digital network,
which means that it can be monitored remotely even after completion of studies andgraduation.
5. The Internet of Things is the future technology that all fields await, including educational institutions, and major institutions are racing to explore its depths and reap
its fruits. Several institutions and technology companies are racing to spread andpopularize the Internet of Things in all areas of our lives. So we can say that theInternet of Things will become a global force, and this technology has achieved an
unprecedented connection to the digital network that included people, machines,
tools, and “things” in general. These communications allow companies to monitor
most of the operations that take place around us.
6. They contribute to ridding us of simple, repetitive tasks daily, focusing on important matters, and leaving machines to do repetitive tasks.
7. IoT technology enables us to automate and comply with ever more stringent international industrial regulations, codes, and standards, by improving movement
within a facility, and tracking hazardous materials, components, and other products. As well as the management of vital contact points, especially in the field offood processing. However, the current price of this technology does not help muchto improve the cost.
8. This technology provides a rich and flexible platform for students, teachers, administrators, and others to explore, learn and interact with the educational system in asuper-intelligent environment.
9. Advanced technology helps students learn new things by supporting educational
goals. It allows students and teachers to share documents online and make changes
in real-time on the screen, helps teachers organize all student resources, and helps
record lessons directly on the computer. It also helps students to access any information they need through a single search of one of the search engines.
10. Technology helps students communicate with teachers using different methods, it
helps teachers to keep track of all students, assign them homework through various
online tools and track their performance. Teachers stay in touch with students all
the time and eliminate any communication gap between them. Technological useof technologies helps students take on multiple roles and take responsibility for

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learning and gives them freedom of expression and work in a modern and safe
environment.
11. Contribute to education anytime and anywhere, as things play a vital role in building society using different platforms on the internet, advanced technology helps
teachers monitor students’ progress, making it possible for learners to gain knowledge from anywhere and at any time. It allows students and teachers to keep intouch via various means, check out upcoming media and events away from theclassroom, and even respond to jobs. It is one of the applications that provide asecure network and complete privacy as it allows to store unique ideas and ensurecomplete confidentiality [29–30].
2.7

The role of the Internet of Things in solving education problems

a. Availability in Wide Geographical Coverage: Wired and wireless networks now
cover most of the population, creating unlimited platforms that increase availability.
Here, the Internet of Things helps learners globally to make available locally andglobally, whether with the appropriate content or by choosing teachers anywhere.
The solutions offered by the Internet of Things allow a large number of learners
in China, for example, to learn English through text messages and audio lessons,despite the scarcity of specialized and qualified local teachers.
b. Timely Availability, Autonomy: The Internet of Things offers educational alternatives anytime and anywhere, unlike traditional classrooms. For example, in someisolated villages, some university professors deliver lessons through podcasts thatallow learners to access them anytime and anywhere and learners interact withthem through text messages in an independent way for each learner in the learning
process.
c. Adaptation: Educators can use IoT technology to adapt education to each learnerindividually, unlike the one-to-many method that provides the average needs of thegroup and, accordingly, reduces the importance of adapting education to the groupof learners, the Internet of things helps to change that by:
1. Customization: Each student has his learning style and a different learning speed.
Therefore, teachers are often unable to follow up and respond to these differences in the learning style and speed of learners’ achievement. Here, we find that
the Internet of Things (IoT) technology can provide real data directly at the sametime as learning through simple formative assessments via wireless technology,
which helps teachers to adapt and customize education for each student individually. For example, interactive learning methods allow different ways of adjusting
the difficulty levels, number, type of hints, etc, for students based on observing
their different reactions to many problems.
2. Collaboration: Students often understand and apply concepts best through discussion and collaboration with their peers. However, traditional learning environments do not allow this, especially with large class sizes. So IoT helps learners
get their content, share it with peers, and share different learning paths throughcollaboration [31–32].

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2.8

Applications of the Internet of Things in education

IoT has contributed to the development of many services that contribute to the development of the educational process, including the following:
1. Smart education: Smart education is a special teaching method that is completely
different from the traditional method, where the teacher can add a lot to the educational process through various means with the help of electronic tools and helps thelearner choose from a wide range of educational aids, and provides quality content
on 24 x 7 range.
2. Smart Classroom: The smart classroom is the place for comprehensive educational activities and where there is learning, teaching, and assessment all happen
differently and effectively. The Internet of Things also provides the ability to control smart classroom components of electronic gadgets, such as a digital screen,
projector, and Internet-enabled devices that enable smart learning to be managed
successfully.
3. A better educational experience, as the main function of the Internet of Things, isto enable understanding between things, such as understanding one device withanother, and it saves the time and effort of the teacher in connecting, managing, andcontrolling devices, and helping him to provide a distinctive learning experiencethat can be transferred to learners.
4. Proof of attendance: Each student is verified on a dedicated cloud application using
data collected from each student’s RFID reader or scanner.
5. SMS Alerts: Automated receipt and access alerts are sent to parents via mobile
phone.
6. Smart School Bus Management School vehicles can be effectively tracked andmanaged, and the bus’s compliance with the road is monitored. A student’s parent
or principal can get alerts when a bus driver is off the road to refuel, time, and othertransportation safety with the help of smart IoT technologies. And the ability to
track live by displaying the bus journey live on the map, allowing more visibility
for safety.
7. Emergency management: In the event of an emergency, safe paths can be identified
inside the school for students to cross safely, and school buses can be routed on theroad at the time of accidents or disasters. In addition to detailed information about
students, such as blood type, home address, parent’s phone, sensing fires insidelaboratories, halls, classrooms, room temperature sensors, the nearest ambulance
point, and so on. With many students in educational institutions, monitoring themis a difficult task, moreover, students in educational institutions are more vulnerable so smart security should be used compared to employees in their workplaces.
The Internet of Things can greatly enhance the security of schools, colleges, andany other learning centers. With the help of technologies such as 3D positioning,
students can be monitored 24/7 and their presence reported at any time in addition
to providing the option of distress buttons, with these technologies the alarm canbe sounded when needed. And wireless door locks that can control the doors ofeducational institutions remotely.

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8. Improving operational efficiency in educational institutions that have many common stakeholders and the Internet of Things, thus helping to manage those relationships efficiently in terms of tracking students, staff, resources, equipment, anddevices. The operation of these things can be effectively managed by implementing
effective operational management process techniques that lead to overall successacross IoT applications.
9. Reducing the cost: The main expenditures in the educational process represent
a large proportion of the budgets of countries that consider education one of thebasics of nation-building. The obsession with controlling these expenses andreducing them in a way that does not affect the quality of education remains a realconcern, and when the Internet of Things is applied in the educational process, itleads to enabling automatic communication between the educational sectors andthe different systems and things in them. It will automatically enhance the ability toclosely monitor, thus reducing overall expenses.
10. RELIABILITY IoT applications enhance the reliability of the system and its components and the ability to manage them efficiently.
11. Educational applications: educational applications that benefit from the Internet ofThings can be considered powerful creative tools that are transforming the normal
way of teaching and learning. It also enables teachers and students to create 3Dgraphic books that feature videos and the ability to take notes. In addition to thelarge number of educational games it provides. Where these games provide many
features that offer interesting possibilities in teaching and learning and this is what
can develop the desire to learn more than ever. The educational applications thatcan be provided through the Internet of Things are many and varied, and the mostprominent of these applications are poster boards that have been developed using
the Internet of Things, where they are using multimedia stickers, as it is possible
to easily create virtual posters that combine images, audio, video, text, and hyperlinks. Smartboard applications can help teachers to explain lessons more easily
with the help of online presentations and videos, students are encouraged to treat
interactive games as a powerful platform. Web-based tools and software help students learn more effectively [33–35].
12. Increasing Efficiency In many schools and colleges students spend a lot of timeon activities that do not add any value to the primary purpose of their actual existence. For example, attendance of students should be taken several times a day
and additionally, this data should be sent to the central office for various purposes.
But the Internet of Things can put an end to this inefficient system. With the helpof IoT circulating devices, this data can be collected and sent to the central telephone server automatically eliminating the need for any human intervention. Dueto this revolutionary shift towards the Internet of Things, the tedious tasks ofteachers and students can be reduced allowing them to focus more on teaching andlearning [36–42].
13. Smart Classroom: Internet of Things technology can control smart classroom components from electronic tools such as digital screens, smart board control, tablets,
printers, e-books, student ID cards, projectors, and Internet-enabled devices, which
successfully achieves smart learning [34–36].

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3

A practical framework

This framework deals with two axes. The first axis analyzes the characteristics ofthe research sample in terms of achievement, type of work, and general and precise
specialization. The second axis provides an analysis of the data of the answers of theresearch sample to the questions of the questionnaire, which included (6) questions andwas distributed to a random sample of professors and students of Iraqi universities andamounted to (194) individuals; after it was presented to a group of experts to prove its
sincerity. It was prepared electronically according to Google Forms, the form https://forms.gle/4rjN2f7Zs68jGQbZ9 and sent via social media groups (WhatsApp, Viber,
Telegram) to university students and professors.
3.1

The first axis: analysis of the characteristics of the research sample

A-The academic achievement of the respondents: Table 1 shows the academic
achievement of the respondents, where the category of Ph.D. holders represented thehighest and most prominent category of those who answered the questionnaire. Their
number was (84) or 43.30% of the total. The second place was for those who obtained
a master’s degree, as their number was (68) with a rate of 35.05%, and the third for abachelor’s degree was (42) with a rate of 21.65%.
Table 1. Academic qualification of the respondents
No.

Number

Percentage

1

Doctorate

Academic Achievement

84

43,30

2

Master

68

35,05

3

Bachelor

Total

42

21,65

194

100%

B-Type of employment: Table 2 shows the type of work of the respondents. Where
it represented the highest number and percentage of the category of teachers, as their
number reached (152) and a rate of 78.35%. The student category ranked second, withseveral (42) and a rate of 21.65%.
Table 2. Type of work for respondents
No.

Type of Work

Number

Percentage

1

152

152

152

2

42

Total

42

42

194

100%

C-General and specific jurisdiction: Table 3 shows the type of general and exact
terms of reference for the respondents. Where it represented the number of specializations (17) specialization. The highest number and percentage of history specialization was (34), with a rate of 17.53%. Then the specialization of educational andiJET ‒ Vol. 18, No. 01, 2023

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psychological sciences, as number reached (31) with a rate of 15.98%. Then the Arabic
language specialization, as their number reached (24) with a rate of 12.37%. The lowest
percentage was represented by the specialization of physical education and specialization of education (kindergartens), as their number reached (1), at a rate of 0.55%.
Table 3. The general and specific specialization of the respondents
No.

Number

Percentage

1

History (ancient, Islamic, modern, contemporary)Specialization

34

17.53%

2

Educational and psychological sciences (educational administration,
educational psychology, measurement and evaluation, curricula andteaching methods, developmental psychology)

31

15.98%

3

Arabic language (linguistic studies, literature, language, rhetoric, andstylistics)

24

12.37%

4

Islamic sciences (Quranic studies, Tafseer, Islamic jurisprudence,
hadith, theology, Islamic faith)

22

11.34%

5

Geography (natural, human, methods of teaching geography)

21

10.83%

6

Science (physics, chemistry, biology, botany, science teaching
methods)

17

8.76%

7

Information and libraries (information services, information
technologies, libraries)

14

7.22%

8

English (literature, linguistics)

11

5.67%

9

Kurdish Language (Methods of Teaching of the Kurdish Language)

4

2.06%

10

Medicine (surgery, dentistry, veterinary)

3

1.55%

11

Media (digital media, press, radio, and television)

3

1.55%

12

Common Law (criminal procedure, civil law)

2

1.03%

13

Agricultural engineering (horticulture and landscaping, animal
production)

2

1.03%

14

Management and Economics (Management, International Economy)

2

1.03%

15

Arts (Design, Architecture)

2

1.03%

16

Physical education (physical movement)

1

0.51%

17

Education (Kindergarten)

1

0.51%

194

100%

Total

3.2

The second axis: data analysis of the answers of the research sample to thequestionnaire questions

1. Do you use the internet in education? Table 3 shows the number and percentageof respondents who use the Internet in education. The number of those who use theInternet (191) represented 98.45%, which is the highest percentage. The number of
those who do not use the Internet and teach in person is (3), at a rate of 1.55%.

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Table 4. Number and percentage of respondents who use the Internet in education
No.

Answer1

I use the internet in education

2

I do not use the internet in education

Total

Number

Percentage

191

98.45%

3

1.55%

194

100%

2. Identify the electronic devices and equipment you use for education: Table 5shows the electronic devices and equipment used by students and professors in teaching. The highest number and percentage of those who use the smartphone was (184)individuals, at a rate of 97.42%, and the lowest number of those who use smart panels
was (2) individuals, at a rate of 1.03%.
Table 5. Electronic devices and equipment used by the respondents for education
No.

Equipment

Number

Percentage

1

Mobile phone

184

97.42%

2

Computer

179

92.27%

3

Electronic and interactive information sources35

18.04%

4

Digital display

26

13.40%

5

AI

24

13.37%

6

Smart Board

23

11.86%

7

Digital Pen

18

9.28%

8

HD TV

5

2.58%

9

Other equipment

3

1.55%

10

Tablets2

1.03%

Notes: The answer to other devices and equipment, please mention them: the respondents mentioned(advanced presentations (data show), electronic classes, and specialized electronic programs).

3.3. Determine the importance of using Internet of Things technologies in
education: Table 6 shows the respondents’ answers about the importance of using
Internet of Things technologies in education. Where it represented the highest
importance (providing students with academic courses and their sources) with anumber (147) and a rate of 75.77%. Of great importance, is number (35) with a rate of
18.04%. The least important (guaranteeing students’ participation in various electronic
and interactive educational activities) was represented by the number (81), with a rateof 41.75%. Of great importance, with a number (of 22) and a rate of 11.34%, it is notimportant.

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Table 6. Respondents’ answers about the importance of using Internet of Things
technologies in educationNo.

The Importance of Using IoT

Very
ImportantMediumImportance

Not
Important

No.

%

No.

%

No.

%

1

Providing students with courses and theirresources

147

75.77

35

18.04

12

6.19

2

Giving electronic lectures at the appropriate
times

141

72.68

38

19.59

15

7.73

3

Effective presentation of educational content
using modern presentation technologies

135

69.59

44

22.68

15

7.73

4

Ensuring speedy performance of assessment
tests and obtaining immediate results

135

69.59

38

19.59

21

10.82

5

Effectively presenting electronic lectures

134

69.07

54

27.84

6

3.09

6

Recording electronic lectures and making
them available to students

122

62.89

63

32.47

9

4.64

7

Digital simulation for all students regardlessof where they are

112

57.73

63

32.47

19

9.79

8

Ensuring the participation of all students inthe lectures and preparing lists for that

106

54.64

69

35.57

19

9.79

9

Ensuring the participation of students ingroups and making the educational material
available to them

106

54.64

72

37.11

16

8.25

10

Ensuring students’ participation in various
electronic and interactive educational
activities

81

41.75

91

46.91

22

11.34

3.4. Determine the role of using Internet of Things technology in solving
education problems: Table 7 shows the respondents’ answers about the role of using
Internet of Things technology in solving education problems, as the highest paragraph(giving lectures in light of crises, especially the spread of infectious diseases in lightof the Coronavirus crisis) represented a significant role with several (156) and apercentage of 80.41% and represented the highest paragraph (the ability of the professors to adapt to the students and identify their levels and assign each student a special
learning style). Of the paragraphs that have no role in solving problems, a number (28)
and a percentage of 14.43%.

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Table 7. Respondents’ answers about the role of using Internet of Things technology
in solving education problems
No.

4

The Role of Using IoT Technology inSolving Education Problems

Major Role

Medium Role

Has No Role

No.

%

No.

%

No.

%

1

Giving lectures in light of crises, especiallythe spread of infectious diseases in light of
the Coronavirus crisis

156

80.41

35

18.04

3

1.55

2

Availability at the local and global level,
whether with the appropriate content orchoosing professors anywhere in the world

119

61.34

72

37.11

3

1.55

3

The lectures are presented interactively,
recorded, and made available to students
at any time, unlike traditional education
which is limited to a specific time

116

59.79

63

31.96

16

8.25

4

The Internet of Things helps students
access and share their content with peers
and facilitate collaboration

100

51.55

78

40.21

16

8.25

5

The ability of teachers to adapt to students,
recognize their levels, and assign each
student a special learning style

63

32.47

103

53.09

28

14.43

Conclusion

The IoT means all the devices and tools that we use in our daily lives that can connect
to the Internet and are managed through the application of smartphones or computersand others. It is a modern, global technology based on the Internet whose purpose is toprovide an information technology infrastructure. Among the most prominent benefitsof the Internet of Things and its importance, organizations can use this technology to
reduce costs, and introduce products through advertising and promotion with ease. It isan effective way to overcome some problems and strengthen the relationship between
the student and the teacher. Also, it saves a lot of time and data, in addition to being
low in cost, and frees people from the restrictions of time and place. Among the mostimportant characteristics of the Internet of Things: Interaction, heterogeneity, dynamicchanges, intelligence, sensing, massive capacity, communication, and others. The IoTarchitecture consists of the device layer, the gateway and network layer, and the management service layer. Among the most important structural components of the Internetof Things architecture are identity verification, sensors, and a central server. Amongthe most important components of the Internet of Things: are solid materials, Internet of things software, technology, and Internet of Things protocols. Among the mostprominent Internet of Things search engines are Shodar, Thingful, and Wais. Amongthe most important applications of the Internet of Things in education: are smart education, proof of attendance, emergency management, improving operational efficiency,
increasing efficiency for students, and the Edmodo educational network. One of theiJET ‒ Vol. 18, No. 01, 2023

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most important contributions of the Internet of Things to sustainable development isbuilding smart cities and protecting the environment. IoT technology appeared in theyear 2000 AD to improve sales management and monitoring. As a result of the rapid
developments, this technology has been used in all areas of life, including education
by relying on electronic devices, equipment, and software, including smartphones,
handheld devices, smart boards, electronic classes, and high-definition television, etc.
Internet of things technology has several characteristics, the most important of whichare (environmental interaction, dynamic changes, intelligence, sensing, interconnection, huge capacity, safety, etc.). One of the advantages of using the Internet of Things
technology in education is that it is an effective way to overcome some of the problems
facing the learner and helps him to overcome the barriers of time and space and enables
him to successfully control the learning management from a distance. The use of IoTtechnology in education helps students learn new things by supporting educational
goals and helps them communicate with teachers using different methods. Among theapplications of the Internet of Things in education (smart education, smart classes,
proof of attendance, text message alerts, emergency management in educational institutions, improving operational efficiency in educational institutions, educational applications, etc.).

5

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6

Authors

Suaad Hadi Hassan Al-Taai, Ibn Rushd For Human Sciences, University of
Baghdad/College of Education, Baghdad, Iraq.
Huda Abbas Kanber, Ibn Rushd For Human Sciences, University of Baghdad/
College of Education, Baghdad, Iraq.
Waleed Abood Mohammed al-Dulaimi, Ibn Rushd For Human Sciences,
University of Baghdad/College of Education, Baghdad, Iraq.
Article submitted 2022-09-12. Resubmitted 2022-10-28. Final acceptance 2022-10-29. Final version
published as submitted by the authors.

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

A New Resource Recommendation Method forExperiential Leaching Based on the Completion Degreeof Online Learning Tasks
https://doi.org/10.3991/ijet.v18i01.37129

Ligang Jia(*)

Shandong University (Weihai) Art College, Weihai, China
201999800028@stu.edu.cn

Abstract—Experiential Learning (ExL) is an effective way to consolidate
theoretical knowledge and deepen understandings, and the recommendation of
ExL resources needs to also take the effect of students’ theoretical learning into
consideration. However, existing studies generally ignore the stage-by-stage
assessment of students’ completion of online learning tasks, and the recommendation performance of existing resource recommendation models for ExL is notsatisfactory enough. Therefore, the recommendation method needs to be innovated, and the interpretability of recommendation results is facing challenges.
To respond to these issues, this paper studied a new resource recommendation
method for ExL based on the completion degree of online learning tasks. At
first, the paper gave the principle of recommending ExL resources based on thecompletion degree of online learning tasks, and built an online learning taskcompletion degree prediction model. Then, this paper adopted a bi-directional
GRU network model based on attention mechanism to analyze the recent online
learning behavior sequence of students and attain the completion degree of students’ short term learning tasks. After that, a knowledge map representing ExL
resources and the correlation of knowledge attributes was drawn; by combining
the completion degree of both the short-term and long-term learning tasks, the
ExL resources suitable for students were recommended to them. At last, experimental results verified the effectiveness of the constructed model.
Keywords—online learning, task completion degree, Experiential Learning(ExL), learning resource recommendation

1

Introduction

With the advent of Internet and big data, the sharp increase in data volume and thecontinuous optimization of information collection and sharing methods have resulted
in the problem of information overload, and online learning platforms have to develop
learning resource recommendation systems to deal with this problem [1–6]. The learning resource recommendation systems can analyze students’ learning behavior, minethe personalized learning requirements of students based on their completion degree of
online learning tasks, and help them find learning resources that are suitable for theirlearning level and might attract their interests [7–11].

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

Experiential Learning (ExL) is an effective way to consolidate theoretical knowledge and deepen understandings. To assist students to enhance their hands-on ability
and get good learning effect, the recommendation of ExL resources should be able toconnect theory with practice and it must take the effect of students’ theoretical learning
into consideration [12–21]. Therefore, the recommendation method needs to be innovated, and the interpretability of recommendation results is facing challenges.
Zhang et al. [23] pointed out that as smart education is being extensively appliedin higher education, now the recommendation of personalized learning resources
has become an important research field of smart learning. The authors proposed a
Q-LRDP-D (Learning Resource Difficulty Prediction and Dijkstra based on Q matrix)
algorithm. At first, the learning resources were modeled based on the Q matrix theory, and the students’ learning difficulty was predicted by the long short-term memory(LSTM) algorithm of the learning resource difficulty prediction module; then, according to the requirements of the teaching units, the knowledge points to be learnt werecombined for cyclic prediction so as to form a directed path map of learning resources;at last, the shortest path algorithm was used to recommend the minimum learning
resources suitable for students’ learning level to complete the learning tasks. Dien et al.
[24] constructed a deep matrix decomposition model which is an extension from standard matrix decomposition, and used it to recommend learning resources based on thecapabilities and requirements of learners. The authors tested the model on two sets ofexperimental data, one dataset was a college student’s learning results of recommended
courses, another dataset was the learning resource data of five users; at last, the authors
gave a few useful suggestions for the learners. In terms of ExL, Zhang and Guan [25]researched the assessment background, characteristics, contents, and methods of ExL
and gave a few application examples. Cardona Zapata and López Ríos [26] introduced
the potential of a heuristic V diagram designed as a theoretical-methodological toolthat can increase the potential of data acquisition system in ExL, aiming to solve theshortage in teaching resources for experimental scenarios.
After reviewing relevant literatures, we found that field scholars in the world haveconducted a lot of research on the influence of students’ online learning preferenceson their learning effect, but there’re differences in the determination of factors affecting students’ completion of online learning tasks, and few of them have given stageby-stage assessments on students’ completion degree of online learning tasks, or built
resource recommendation model for ExL. To fill in this research blank, this paper aims
to develop a new resource recommendation method for ExL based on the completion
degree of online learning tasks. In the second chapter, this paper gave the principleof recommending ExL resources based on the completion degree of online learning
tasks, and built an online learning task completion degree prediction model. In the thirdchapter, this paper adopted a bi-directional GRU network model based on attention
mechanism to analyze the recent online learning behavior sequence of students andattain the completion degree of students’ short term learning tasks, and a knowledge
map representing ExL resources and the correlation of knowledge attributes was drawn.
In the fourth chapter, the paper combined the completion degree of both the short-termand long-term learning tasks, and generated suitable ExL resources for students andmade recommendations. At last, experimental results verified the effectiveness of theconstructed model.

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2

Prediction model based on completion degree of online
learning tasks

Figure 1 shows the principle of recommending learning resources for ExL based on
online learning. At first, the learning resource recommendation algorithm analyzes thetarget student’s online learning behavior based on texts; then, it calculates the potential correlation between the target student and the ExL resources, searches for ExL
resources that fit the student’s completion degree of online learning tasks, and screens
them; at last, recommendations are made for the target student.
In order to assess students’ completion of online learning tasks stage by stage, thispaper collected the data of students’ online learning task scores, the number of teaching
videos watched, the watching frequency of teaching videos, and the average watchingtime of each teaching video. These data can reflect the differences in the knowledge
structure of students formed by their learning styles and theoretical knowledge review
states before the learning of ExL resources in the online learning environment.

Fig. 1. Principle of recommending ExL resources based on completion degreeof online learning tasks

When building the prediction model, at first, this paper assumed that the final scoresof the online learning tasks reflect the comprehensive completion degree of students forthe learning tasks of each theoretical learning topic, it can be considered that for students with different completion degrees of online learning tasks, their final scores might
be the same. The formula below gives the full probability formula of the online learning task completion degree prediction model, which describes the probability of thecompletion degree of primary task constituted by the completion degrees of sub-tasks.

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

GU p 

 GU (r )  GU ( p|r )
n

i 1

i

i

(1)

Assuming: GU represents the probability that a student has completed a certain
learning task; p represents an online learning unit task; GUp represents the probability
that the student has fully grasped the learning topic corresponding to task p; ri represents the quiz sub-task corresponding to task p; n represents the number of quizzes.
When p represents the task in the class, then it can be considered that GUp is the probability of the student getting full score in the online learning task.
At this time, the prediction model hadn’t involved the collected data of the online
learning behavior of students. In this paper, a 5-dimensional vector q was set, and thefunction g about the online learning behavior of students was equivalent to GU(p|ri),which satisfies GU(p|ri)=g(U) and its value range is [0,1]; let U=<Us,Um,Ug,Up,Ko>,then the expression of function g is:
g (U )  sigmoid (U  q )

(2)

Combining the above two formulas, we can get the formula for calculating the
expected completion degree of learning tasks:
GU p 

 GU (r )  sig (U  q)
n

i 1

i

i

(3)

Assuming: qi′ represents the weight coefficient of learning task topic i;
Q   q1, q2 ,.... qn  represents the weight coefficients of all learning task topics. Then, Q
was trained with the minimization of the variance of the difference between GUp andPe′GU ′p as the objective, and the formula below gives the expression of the optimization
training process:min GU p  GU p

2(4)

By combining Formula 1, Formula 2, and the above Formula, we have:min GU p  GU  G

2

(5)
1

Assuming: GU=[GU(r1), GU(r2),...GU(rn)], G   sig (U ·q1), sig (U ·q2 ) ... sig (U ·qn )  ,the constraint of the optimization training process is: sig (U  q)  1
n

i 1

i

(6)

As important independent variables for predicting the completion degree of learning
tasks, the tags of the data collected from four aspects are multidimensional. To increase
the accuracy of prediction results, after the collected data were pre-processed, this paper
adopted the multivariate linear regression to fit the prediction model. Assuming: S represents the total number of visits of the videos of all sub-tasks under a certain learning

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

task topic; M represents the total number of visits of the videos of all sub-tasks under
the learning task topic within a fixed time period; U represents the average watchingtime of the videos of all sub-tasks under the learning task topic within the fixed time
period; E represents the average score of all sub-tasks under the learning task topic,
then the expression of the regression model is:
GU p  59.447  0.521  S  0.635  M  3.215  U  6.074  P

3(7)Knowledge map of ExL resources

To capture students’ completion degree of recent online learning tasks, this paper
built a bi-directional GRU network model based on attention mechanism to analyze therecent online learning behavior sequence of students and attain the completion degreeof students’ short term learning tasks. The completion degrees of both the short-termand long-term learning tasks were combined to recommend suitable ExL resources
to students. To enrich the correlation between the ExL resources, this paper plotted aknowledge map to show the ExL resources and the correlation between the attributesof the knowledge in the ExL resources, and attained the knowledge vector of ExL
resources that contain the information of the knowledge attributes of ExL resources, atlast, based on the attained vector, the recommendations were made.
Assuming: V={v1,v2,...} represents the set of students; U={u1,u2,...} represents theset of ExL resources, the formula below gives the definition of B={bvu|v∈V,u∈U}, theinteraction matrix of ExL resources based on students’ implicit feedback:1, if int ER (v, u ) is OB
bvu  0, otherwise(8)

If the value of bvu is 1, then it indicates that there is a possibility of clicking, browsing, watching, or other types of interactions between student v and ExL resource u.
Assuming: triplet (  f,t,r) represents the entity-relation tuple in knowledge map H; σand T are the set of entities and the set of relations in H. In this paper, the purpose of
establishing the ExL resource recommendation model is to predict the possibility ofpotential interactions between student v and the unknown ExL resource u. Assuming:bvu′ represents the probability that student v would visit ExL resource u, Ψ represents
the relevant function parameter, then the prediction function can be represented by
bvu  G (v, u;  ).
The completion degree of online learning tasks is often affected by short-term factors. To improve the recommendation performance, the ExL resource recommendation
model constructed in this paper not only can fully consider the completion degree ofshort-term online learning tasks, but also can extract the correlations between students,
ExL resources, and knowledge attributes in the knowledge map through the RippleNetalgorithm, then it could apply the constructed knowledge map and the extracted correlations to the ExL resource recommendation tasks.

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

Assuming: B represents the interaction matrix; the formula below defines the set offirst l ExL resources recommended by the RippleNet algorithm to student v:

 vl  r | ( f , t , r )  H and f   vl 1 , l  1, 2,..., F(9)Student v’s TupleSet is defined as:

 vl  ( f , t , r ) | ( f , t , r )  H and f   vl 1 , l  1, 2,..., F(10)Student v’s RippleSet is defined as:
Ov  (v, l , p ) | ( f , t , r )   vl and p   f , r , l  1, 2,..., F(11)

Assuming: c represents the dimension of eigenvectors u and f corresponding to the
ExL resources and the knowledge attributes; for resource u and the knowledge attributef corresponding to u in the knowledge map, at first, a cross matrix D ∈Rc×c was built as: u (1) f (1)  u (1) f ( c )TD  uf   u ( c ) f (1)  u ( c ) f ( c )


(12)

Figure 2 gives a diagram showing the cross-compressed units. Assuming: q ∈Rc represents the weight of the cross-compressed unit; τ ∈Rc represents the bias parameter,
then the formula below gives the expression of the output of the cross-compressed unit:
uout  DqUU  DT q FU   U  uf T qUU  fu T q FU   Uf out  DqUF  DT q FF   F  uf T qUF  fu T q FF   F(13)Fig. 2. Diagram of cross-compressed unit

By adjusting parameters q and τ, the two steps of the semantic matching of knowledge attributes in the knowledge map and the recommending of ExL resources could
be completed at the same time. At first, this paper processed ( f ), (t), and (r) via the
deep semantic matching mechanism, then f and t were spliced, and the dimension
was reduced to c to attain the predicted r, denoted as ṙ; assuming DDu represents

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the cross-compressed unit, DGJ represents the multilayer perceptron and it satisfies
DGJ(a)=ε(Qa+τ), then there are:f = DDu (u , f )[ f ](14)

r = DGJ (r )(15)  f  r  DGJ  DGJ      t   (16)Assuming: ε represents the nonlinear activation function the sigmoid function; atlast, the similarity function glh was used to evaluate the prediction results:
SIM (r , r)  glh (r , r)   (r T r)

4

(17)

ExL resource recommendation model

In the ExL resource recommendation module, the students’ completion degree ofshort-term learning tasks in the recent online learning behavior sequence was learnt
based on the cyclic neural network, the attention mechanism was introduced into thebi-directional cyclic neural network to optimize the ability of the network model tolearn the completion degree of short-term learning tasks, and the structure of the modelis shown in Figure 3.

Fig. 3. Bi-directional cyclic neural network with attention mechanism introduced

Inputs of the ExL resource recommendation model were the first m learnt ExL
resource sequences in the recent online learning behavior sequences, denoted as umLR;
assuming: ar represents the input vector of the r-th time step; fr–1 represents the information of the previous time step r–1; Z and V represent parameter matrixes; then when thetime step is r, the calculation formulas of the update door and reset door are:46

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

cr   (Q c ar  V c f r 1 )(18)

sr   (Q s ar  V s f r 1 )(19)

Assuming: ⊗ represents the Hadamard product; fr–1 represents the hidden layer ofthe previous moment; then the calculation formulas of ḟr (the hidden layout output of
the cyclic neural network at the current moment) and fr (the final output of the hidden
layer) are:fr  tanh(Q f ar  V f ( f r 1  ar ))(20)

f r  (1  cr )  f r 1  cr  fr

(21)

For the final output fr, the amount of information that needs to be forgotten by fr–1,
and the amount of ḟr that needs to be added were both controlled by cr. Assuming: F*
represents the hidden layer output of the cyclic neural network; qT represents the parameter matrix; s represents the cyclic neural network with weight taken into consideration;then the formula below gives the expression of the introduced attention mechanism:  softmax(qT tanh( F  ))(22)s  F  T(23)After that, through the multilayer perceptron, the completion degree of short-term
learning tasks with weight taken into consideration was attained, and the process of
attaining the completion degree could be written in a simple form as:m
FR = DGJ (uLR)

(24)

Because the inputs of the ExL resource recommendation model constructed in thispaper were eigenvectors v and u that describe the student and the ExL resource andthe sequence umLR of ExL resources that recently learnt by the student, if the eigenvector of student v is known, then the eigenvector of student v* after subjected tocross-compression and processed by the multilayer perceptron is:
vdd = DDv (v, f )[v]

(25)v  DGJ ( DGJ ( DGJ (vdd )))(26)

Similarly, the processed u* could be attained through the following steps:

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udd = DDu (u , f )[u ](27)u   DGJ ( DGJ ( DGJ (udd )))

(28)

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

Assuming: μ1 represents the weight of the completion degree of short-term learning
tasks; FR(umLR) represents the student’s completion degree of short-term learning tasks,
then the following formula gives the comprehensive recommendation of ExL resources:bvu  (1  1 )  vu T  1  FR(uLR m )u T(29)Assuming: ψ represents the cross-entropy loss function; WQTO represents the loss
value of the ExL resource recommendation model; WQLHv and WQLHu represent theloss values of the fitting degree of the “student – relation – ExL resource knowledge
attribute” triple and the “ExL resource – relation – ExL resource knowledge attribute”
triple; ||Q||22 represents the regularization term; μ2 represents the coefficient of the regularization term, then the loss function of the constructed recommended model is:
WQ = WQTO + WQTO + WQLH=∑v∈V , u∈U5v

ψ (bˆvu , bvu ) +u∑ SI (r, rˆ) + ∑ SI (r, rˆ) + µ Q( f , t , r )∈Hv( f , t , r )∈H

u

2

2

(30)

2

Experimental results and analysis

Figure 4 shows the fitting results of four types of data: the score of online learning tasks, the number of watched teaching videos, the watching frequency of teaching
videos, and the average watching time of teaching videos. The results showed that theeffect of the model designed in this paper is significant. Next, comparative experiment
was designed to compare the performance of the logistic regression model and themodel proposed in this paper in predicting the completion degree of online learning
tasks. Table 1 lists the prediction accuracy of different models. The fitting degree andprediction accuracy of the proposed model are both higher, indicating that it is feasible
to predict the students’ completion of online learning tasks based on the four types ofdata mentioned above. This is because after a student has finished all online learning
tasks, the prediction of his/her completion degree of online learning tasks depends onthe differentiated online learning behavior of the student. That is to say, the summative
assessment and prediction of the completion degree of online learning tasks wouldn’t
affect the learning process of this student.

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

(a)

(b)

(c)

Fig. 4. Fitting effect of each type of collected data

Table 1. Prediction accuracy of different models
Training Accuracy

Prediction Accuracy

Avg

s.d.

Avg

s.d.

The proposed model

0.528

0.069

0.536

0.058

Logistic regression model

0.41

0.47

0.43

0.041

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Table 2. Model prediction accuracy for different learning topics
Learning Topic

Model Accuracy
Logistic Regression Model

The Proposed Model

Topic10.48

0.56

Topic20.49

0.59

Topic30.43

0.57

Topic40.47

0.59

Topic50.41

0.57

Topic60.49

0.63

Table 3. Experimental results of different models in recommending ExL resources
Model

Dataset 1

Dataset 2

AUC

ACC

AUC

ACC

SVD++

0.81520.80320.625

0.7152

CNN+K-Means

0.86950.81590.63920.7485

CNN+FP Tree

0.83250.83620.61470.7362

GBDT

0.84710.81710.68530.7481LDA

0.82690.86290.62950.7596

The proposed model

0.83260.83610.68470.7158

Table 2 shows the model prediction accuracy for different learning topics. Based on
the 4 types of data collected after the student finished the learning tasks of Topic 3, theproposed model attained a prediction rate close to 60%, while the prediction rate of thelogistic regression model was lower than 50%; when the student began the learning
tasks of Topic 4, the prediction rate of the completion degree of the proposed model
far exceeded that of the logistic regression model, in other words, regardless of thecollected data is all or part of the learning behavior data, the proposed model can attainbetter prediction effect of the completion degree of online learning tasks, and this makes
it possible for the ExL resource recommendation system to predict students’ basic levelof theoretical learning in advance and provide corresponding intervention measures.
Table 3 gives the experimental results of different models in recommending ExL
resources. Specifically, the results include the recommendation AUC and accuracy ofdifferent models attained in the same experimental environment on the training set. Thesemodels are: SVD++ (reference algorithm 1), CNN+K-Means (reference algorithm 2),
CNN+FP Tree (reference algorithm 3), GBDT (reference algorithm 4), LDA (reference
algorithm 5), and the proposed algorithm. According to the table, compared with otherrecommendation algorithms, the proposed model attained the best results in terms ofboth performance indicators. On dataset 1 (for science and engineering students), compared with other models, the proposed model showed a 1.75% improvement in termsof AUC, and a 1.18% improvement in terms of accuracy. On dataset 2 (for liberal arts
students), compared with other models, the proposed model obtained a 1.92% improvement in AUC and a 1.31% improvement in accuracy, indicating that the introduction50

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Paper—A New Resource Recommendation Method for Experiential Leaching Based on the Completion…

of knowledge map can improve the recommendation performance of the ExL resource
recommendation model, and this has proved the superiority of the proposed model.

Fig. 5. Accuracy of different models in recommending ExL resources

Fig. 6. Recall rate of different models in recommending ExL resources

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After experiment, the accuracy and recall rate of different models in recommending
ExL resources are shown in Figure 6. As can be seen from the figure, compared withother recommendation models, the proposed model showed great improvements in bothindicators, wherein when the list resource quantity is 10, the improvement in accuracyof the proposed model is 10.5% compared with other models, and the improvement
in recall rate is 7.8%, which has further proved the superiority of proposed model inrecommending ExL resources.

6

Conclusion

This paper studied a ExL resource recommendation method based on the completion degree of online learning tasks. In the beginning, this paper gave the principle ofrecommending ExL resources based on the completion degree of online learning tasks,
and built an online learning task completion degree prediction model. Then, the paper
adopted a bi-directional GRU network model based on attention mechanism to analyze
the recent online learning behavior sequence of students and attained the completion
degree of students’ short term learning tasks, and a knowledge map representing ExL
resources and the correlation of knowledge attributes was drawn. After that, this paper
combined the completion degrees of both the short-term and long-term learning tasks,
and generated suitable ExL resources for students and made recommendations. In theexperimental part, the fitting results of four types of data (including the score of online
learning tasks, the number of watched teaching videos, the watching frequency ofteaching videos, and the average watching time of teaching videos) were given, which
verified that the effect of the model designed in this paper is significant. Then, theresults of the accuracy of different models verified that the fitting degree and prediction
accuracy of the proposed model are both higher; and the results of the model prediction
accuracy for different learning topics verified that regardless of the collected data is allor part of the learning behavior data, the proposed model can attain better prediction
effect of the completion degree of online learning tasks. Next, the experimental results,
accuracy, and recall rate of different models in recommending ExL resources were
given, which verified that the introduction of knowledge map can improve the recommendation performance of the ExL resource recommendation model, and this had also
proved the superiority of the proposed model.

7

References[1] Liu, Z.Y., Dong, L.Y., Wu, C.L. (2020). Research on personalized recommendations forstudents’ learning paths based on big data. International Journal of Emerging Technologies
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chatbot-LDA recommender system. International Journal of Emerging Technologies inLearning, 15(20): 4–20. https://doi.org/10.3991/ijet.v15i20.15657[3] Mikić, V., Ilić, M., Kopanja, L., Vesin, B. (2022). Personalisation methods in e-learningA literature review. Computer Applications in Engineering Education, 30(6): 1931–1958.
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[4] Rahmania, A., Jesslyn, K., Gui, A., Sari, S.A. (2022). Understanding higher education students continuance intention towards e-learning. In 2022 2nd International Conference on
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compeleceng.2022.108210[11] Zhou, F. (2022). Research on teaching resource recommendation algorithm based on deeplearning and cognitive diagnosis. Journal of Healthcare Engineering, 2022: Article ID:
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[18] O’Connell, B. (2022). Work in progress: Teaching MATLAB through authentic data collection and analysis experiences using self-contained, guided experimental setups with a rangeof disciplinary themes. In 2022 ASEE Annual Conference & Exposition.
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and Education (ISAIEE), pp. 330–333. https://doi.org/10.1109/ISAIEE55071.2021.00086[20] Deng, X., Lei, J., Chen, M. (2021). Application of VR in the Experimental Teaching of
Animation Art. Mobile Information Systems, 2021: Article ID: 4642850. https://doi.
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and Microteaching Method in experimental teaching based on wechat platform. In Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing,pp. 407–411. https://doi.org/10.1145/3448748.3448988[22] Wang, X. (2021). Construction of intelligent experimental teaching platform based on
intelligent control algorithm. In 2021 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE), pp. 228–231. https://doi.org/10.1109/
ISAIEE55071.2021.00063[23] Zhang, L., Zeng, X., Lv, P. (2022). Higher education-oriented recommendation algorithm forpersonalized learning resource. International Journal of Emerging Technologies in Learning,
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s10758-021-09517-2

8

AuthorLigang Jia, teacher at Shandong University (Weihai) Art College, Weihai 264209,
China, mainly engages in the research of college art education. His email address is201999800028@stu.edu.cn.
Article submitted 2022-11-01. Resubmitted 2022-12-07. Final acceptance 2022-12-11. Final version
published as submitted by the authors.

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Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

The Effect of a Proposed Strategy according to the
Design Thinking Model in Mathematics Achievement
and Personal Intelligence among Studentsof Sixth-Class Scientifichttps://doi.org/10.3991/ijet.v18i01.35981

Areej Khuder Hassan(*)

College of Education for Pure Science/Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq
areej.k.h@ihcoedu.uobaghdad.edu.iq

Abstract—Researcher adopted the experimental research method with twogroups (experimental; control) with a posttest; to know the impact of a proposed
strategy according to the design thinking model on the achievement of mathematics
and personal intelligence among students sixth-class scientific. The null hypotheses
were made “there is no statistically significant difference at the significance level(0.05) between the mean scores of students sixth-class scientific (biology) who
learned the mathematics subject assigned to them according to proposed strategy(the experimental group) with students who learned the same material by usual way(control group) in test of achievement”. Community of research represent students
sixth-class scientific (biology) from the General Directorate of Education of KarkhFirst. Research sample was selected (60) students, and the sample was divided into
two groups (experimental) and (control). The equivalence of both groups was madein (previous achievement in “Mathematics”, “level of intelligence”, “chronologicalage”). For collecting data to the experiment, an achievement test was built in of(10) test paragraphs of essay type, plus a scale to measure personal intelligencewith five test paragraphs. Analyzes were conducted the appropriate statistic, andthe psychometric properties of the test were confirmed. Results indicated that students of the experimental group who studied according to the proposed strategy
were superior to the students of control group who studied by usual method.
Keywords—proposed strategy, design thinking model, mathematics, personal
intelligence, students sixth-class scientific1

Introduction

Despite the continuous calls for the adoption of modern strategies in teaching to
make new generations able to keep pace with the rapid developments of the era andface new challenges, we still notice a decrease in the level of achievement among
students and to some extent students still have aversion to mathematics as it is dry, difficult, and incomprehensible, and despite Continuous emphasis on making the student’s

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Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

role active and active, and avoiding memorization and indoctrination, but in fact, thestudent was not focused on the personal aspect [1]. What are his capabilities? and his
feelings about the study? What do you accept to study mathematics? Is he clear andfrank with himself to identify his weaknesses and strengths? Rather, these matters were
implicitly addressed with the modern trends calling for renewal and attention to teaching thinking without focusing on the internal and external personality of the student.
Design thinking is one of the modern models that have proven its efficiency in the fieldof business and engineering [2]. Therefore the problem of the current research can besummarized by answering the following question: Does the proposed strategy according to the design thinking model affect the achievement of mathematics and personal
intelligence among students sixth – class sciences biological? The importance of theresearch, the theoretical aspect is determined by the fact that design thinking model is
one of the most recent models that have been used recently in education. This model
develops students’ creative and imaginative abilities without restrictions or limits.
Develops design capabilities to create new products. Employing scientific knowledge
with practical practices helps to find practical and creative solutions to solve problems. The Design Thinking model helps students think outside the box. Interpersonal
intelligence is one of the important intelligences that students should be trained on.
Students’ possession of personal intelligence increases the motivation to stand out andexcel. Personal intelligence helps to increase self-confidence and thus confidence inpersonal abilities to face all challenges.
As for the practical side, to know if there is an effect of the proposed strategy according to the design thinking model and the achievement of students. Providing a measureof personal intelligence for students of the sixth scientific class that can be used in
the educational field or by other researchers. Directing the attention of mathematics
teachers to adopting new and modern models in teaching that focus on thinking andanalysis in order to design solutions. Draw attention to the importance of students’personal intelligence and its role in increasing self-confidence. The research is limited
on students sixth class science (biological) in General Directorate of Education/KarkhFirst in Baghdad Governorate. Chapter one (complex numbers) from the content of
the mathematics book scheduled for them; edition 1 for the year 2021 and composedby a committee in the Ministry of Education. The 1st semester/year (2021–2022).
Finally, the problem of current research can be summarized by answering the following
question:
Does the proposed strategy according to the design thinking model affect in theachievement of mathematics and the personal intelligence of students sixth-classscience?

2

Theoretical background

2.1

Design thinking

Educators and teachers have been so involved in design thinking that it has beenused in more than sixty USA Universities and Colleges, in the form of lessons, courses,

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Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

workshops, as well as within degree programs, and to promote the development of 21st
century skills for the K+16 class, with support from “IDEO” and “Hasso Blattner”
Institute for Design [3]. So concept of design thinking within academic design dialogue
has been under discussion for more than thirty years, and it’s recent adoption as amethod of innovation and creativity have led to its popularity across disciplines [4, 5].
Is a way of thinking that uses the designer’s methods and feelings to analyze problems
and find appropriate solutions to them, by finding tangible creative products [4, 6]. Also
it is an action plan for sixth-class students (the experimental group) to study based on
five stages (identifying the problem, then collecting information, proposing a method
for solving, implementing the chosen solution, and then providing feedback) [7]. As
mentioned, design thinking means different things to different societies and has two
sides; descriptive models of the design process, based on observational research of reallife or laboratory design activities by individuals or teams. It is a method to be practiced
in industries that strive to provide innovation products or services [8, 9]. According to[10, 11], the term Design Thinking has roots across disciplines, and is not exclusively
associated with engineering, architecture, and related design disciplines in the early
literature that focused on Design, and thus Promoting civic literacy, tenderness, adventure, and cultural awareness. It has been noted by [12, 13] change from discussing,studying “design thinking” as cognitive processes which designers use, to a specificway in which non-designers use design methods—a shift “from design as a science todesign as a mindset”. Thinking, because its core lies in placing participants in contexts
that make them think, act like an expert designer. Design thinking mindset in education;
people who want to innovate and create better experiences, products and services fortheir users or customers may benefit from applying certain situations that can enhancetheir thinking and creativity, so the design thinking mindset can focus on modifying,changing, or enhancing the mindsets of students, or even teachers in building the concept of complex thinking in In the classroom, in the context of teaching and learning.
The design thinking mindset (DT Mindset) based on cognitive psychology that provides guidance for achieving learning goals through teaching activities and encourages
a culture of thinking in education. The teaching process requires students to develop
creativity and innovation through computing skills and build a set of Skills that balance
the expert workers of the future [14–16]. Goal of Design Thinking goes beyond immediate boundaries of a problem to ensure that the right questions are addressed. Process
predicts steps allow participants to analyze, synthesise, divergent, and generate insightsfrom different domains by prototyping and telling stories. In the process of “Design
Thinking” teachers encourage learners seeing limitations as inspiration [17–20]. The
researcher believes that the goal of introducing design thinking to education is to makestudents think with the mentality of a designer in order to solve complex problems thatthey encounter in the classroom or in daily life, which we aspire to be creative solutions,as well as early detection of talented designers to prepare them for the labor market, andthis is what all major countries seek to continue the wheel of progress Keeping abreastof developments in all fields [21, 22]. Design thinking models; there are a number ofmodels put forward by thinkers and educators, including the model which consists ofthree linear sequential stages (understanding, exploration, and application). A modeliJET ‒ Vol. 18, No. 01, 2023

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it consists of six stages between which there is interaction, namely (understanding,observation, viewpoints, visualization, model building, and testing); it can be applied
as a teaching method [23, 24]. A model which consists of seven stages (brief definition, research background, ideas solutions, first model solution, determine the rationale,
implement delivery, learning feedback) which is a linear model. The model consistsof five stages (discovery, interpretation, visualization, experimentation, and observation). Finally, a model was presented at Stanford University in 2016 and it consisted offive interactive stages (empathy, identification, perception, model building, and testing)[25–29]. Researcher thinks that these models, no matter how many and varied theirstages, whether they are linear or circular, the main goal of them is to train students on
the process of identifying the problem, how to collect information, and how to build amodel for the solution in which we are marked by a creative solution. Based on thesemodels, the researcher suggested an educational strategy that consists of five stages
they are (Defining the problem: at this stage, the problem and the goal to be reached are
identified). (Information collection: Information is collected about the problem in allits details and searches for similar problems that were previously solved). (Presenting
ideas and discussing proposed solutions: The process of developing ideas and putting
forward solutions that the designer deems appropriate to solve the problem begins).
(Implementation: At this stage, the implementation process for the chosen solution
begins). (Feedback: At this stage, implementation is followed up to ensure the correctness of the solution steps, with the possibility of modification or development in a waythat enables us to reach creative ways in the solution) [30, 31].
2.2

IntelligenceIntelligence is a hypothetical concept, and that is why scholars differed about
defining specific definitions of it, and because of this difference and controversy, it
has received extensive and extensive study. Rather, it is considered one of the abilities that was most focused on in the last century because it examines the individual
differences between individuals, and despite the discussion of this concept by psychologists, education, sociology, and heredity, they did not reach a clear and agreed
upon definition, and this difference It led to a difference in the way it was studied andmeasured. There are many trends that have been adopted in the study of intelligence,
including the philosophical concept, the biological and physiological concept, and thesocial concept. This difference may be due to the fact that the nature of intelligence is
not materially sensible [32]. Howard Gardner sees according to his theory of multiple intelligences that every human possesses all kinds of intelligences, but the difference between humans by the percentage or level of each intelligence depending on theenvironmental and living conditions and the genetic genes of individuals. According to
the intelligence they possess, Gardner considers that all traditional theories of intelligence do not adequately estimate human intelligence due to the weakness of their tests
that require the individual to solve the problems presented in verbal or verbal formulas
only, and they are suitable for school performance. He also sees that relying on paper

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and pen in measuring performance Intelligent for life tasks, and considers success inlife requires various intelligences, and believes that our best contributions are to know
the intelligences and talents that children possess in order to focus on them [33, 34].
Personal intelligence; Gardner was not the first to talk about interpersonal intelligence.
But rather; Hunt preceded him in (1928) and followed him by Walker & Foley [31],then Gardner addressed it in (1983). The follower of educational and psychologicalliterature finds that personal intelligence has roots in educational psychology theories
such as the theory of ability on Thorndike’s Social Adaptation and Guilford’s Mental
Formation Theory [35]. Interpersonal intelligence is a central institution for other’s
intelligences because it depends on internal pivotal processes that enable individuals to
distinguish between their feelings and thus can build a mental model for themselves.
Introspection of his thoughts and emotions, and the individual becomes aware of his
inner mood and self-esteem, and this helps him in arranging his lifestyle and planning
his future. Those with this intelligence tend to reflect on their problems and capabilities.
This intelligence is considered a certain good for those who possess it to persevere andface obstacles and frustrations because they are able to determine what they can do andwhat they cannot do [36]. Characteristics of personal intelligence: It focuses on theemotional aspect of the response, as it is a basis for understanding the emotional lifeof the human being. It is a mental process that appears through the individual’s understanding of himself and the formation of an effective model for it and relying on thismodel in organizing his life and behavior. The lowest level is the individual’s ability to
distinguish his inner feelings in terms of pain and pleasure, those in the light of which
the individual’s personal characteristics such as introversion or extroversion are determined, while the highest level is the individual’s ability to discover and organize his
feelings. Individuals who possess this intelligence are able to understand their emotions
and direct their behaviors, and tend to be contemplative and private. Personal intelligence is linked to self-reflection through contemplation and awareness of metacognition and is linked to focus and individual evaluation of his ideas [23, 37]. Researcher
believes that personal intelligence shows the individual’s ability to understand himself, determine his goals, this helps him in meeting his needs and helps him adapt to
the reality in which he lives. Training students on this type of intelligence helps themunderstand themselves and determine their capabilities and helps them identify their
weaknesses and thus work to strengthen them. It also generates incentives for them toimprove their academic performance and excel in order to satisfy themselves [38, 39].
2.3

Previous studies

Researcher obtained a few of previous studies that are compatible with nature of thevariables to benefit from them as shown in Table 1.

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60

Hunter, 2006,
Australia

Tabuk & Ozdemir,
2009, TurkyPainter, 2018,
USA

2

3

Name; Year;Country1

No.

teachers

Primary

College

Educational
Level

20

144 Male
students

11 male& female
students

SampleMath.

Mathe.

Mathematics

Subject

Case studyExp.

experimental

Curriculum TypeMultiple
intelligences

Learn & teachsuccessful
mathematics

Independent
Variable

Table 1. Studies dealing with research variables

Project basedlearning;
Achievement

MultipleIntelligences;
Achievement

Dependent Var.

Employing design thinking
strategies for middle school
students led to mastery ofmathematical concepts

there were no statisticallysignificant differences in thedependent variables

no effect of teaching &learning mathematics on
multiple intelligences andachievement

Results

Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

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Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

3

Research methodology and procedures

Researcher choose the “Experimental Research Method”, including experimental
design of two groups (experimental plus control) with a post-test, which is one of real
designs, as it represents proposed strategy (independent vari.) and personal intelligencewith achievement (dependent variables) as shown in Table 2. The research community
consisted of all students sixth class (biological sciences) in the General Directorateof Education of the First Karkh in the province of Baghdad. The researcher chose thedistinguished high school – Al-Khadra for boys from the first Karkh Education Directorate to conduct the experiment on three study divisions, and (B) section was randomly
selected as the experimental group and (C) section as the control group. The experimental group consisted of (30) students, the control group consisted of (30) students[40]. As for the control procedures (internal safety), groups of research were equalized in (age of chronological, intelligence, previous achievement in mathematics). The
external integrity of the design, as the researcher trained the subject teacher to teach theproposed strategy and for the experimental and control groups. The class was taught
to both groups; where the time period was equal for both of them which is (45) days
within the 1st semester/academic year (2021–2022). The number of lessons scheduled
for students is (5 lessons) per week in attendance, and it is equal for the experimental
and control groups. The research tools, both measure of personal intelligence, achievement, were applied to the two groups. The classrooms of the two research groups in thesame place on the first floor were close and similar in terms of physical characteristics
such as lighting, ventilation and seating. As for experimental extinction; it is the impactthat results from leaving a few of students within the research sample or interruption
through work, so “no student” left the study or interrupted work except for the absencesthat two research groups were exposed to in approximately equal proportions.
Table 2. Equivalence of the two research groups in chronological age,
intelligence, and previous achievement
Variableage
intelligence
previousachiev.

Group

Students

SMA

Standard
Deviation

1

30

213.22

6.57

2

30

214.18

5.83

Exp.

30

48.4

4.35

Con.

30

47.3

4.29

1

30

70.4

10.43

2

30

69.3

9.89

T Value
Cal.

Tab.

1.15

1.97

0.17

1.97

0.769

1.97

Indication Level
Not statisticallysignificant

Notes: Exp. = 1; Con. = 2; significance level (0.05).

3.1

The achievement test

After determining the educational content, which is the third chapter of the mathematics book (combined numbers), the behavioral objectives were formulated and their
number was (23) behavioral objectives according to Bloom’s six levels of behavioral

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Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

objectives. A test map was developed to determine the questions for each of the six levelsof Bloom, based on the opinion of the arbitrators, the total number of questions (10)paragraphs of the essay type were determined, they were (4) for the level of remembering,(3) for the level of comprehension, (3) the level of application. Test was given to a groupof academic in a area of mathematics and its teaching methods in order to take theiropinions and observations, after taking their opinions, it was ready to be applied to the
exploratory sample. Table 3 shows the test map with the preparation of behavioral objectives for each chapter. For clarity, understanding of paragraphs, instructions of answer,
calculating the time taken to answer, test was applied to a sample of (100) students fromthe research community and from outside the experiment sample (Al-Mutamayizeen
High School – Al-Harithiya for Boys). To calculate the weighted mean among firstand last three students who took test, (90) minutes was enough [41–44]. In order toobtain statistical indicators for examining the achievement test. First of all; calculating
the coefficient discrimination for each items by adopting its own equation, and it was
found that its value ranges between (0.32–0.69), which they are a good indicator of theacceptance of the items. Difficulty coefficient for the item; the results ranged between(0.27–0.64) and are acceptable, as the sources indicate that any paragraph within the
distribution of the difficulty coefficients ranges between (0.20–0.80). Extracting the psychometric properties of the achievement test; validity coefficient, two types of honesty
were extracted Face Validity which the test was presented to a number of arbitrators inmathematics disciplines and it’s teaching methods. The opinions of the arbitrators weretaken into account in reformulating and amending some paragraphs, and none of themwere deleted. The paragraphs in their final form reached agreement (80%), and thus all
returned. The test items are valid to measure the students’ achievement from the sample.
Content validity: a test map was drawn up in which the objectives were explained at theirlevels with the number of lessons needed to study each topic, and it was presented to anumber of arbitrators and was approved by (80%). One of the content validity indicatorsin addition to what was indicated in the test map.
The Reliability; extracted the stability using the Kuder Richardson Equation – 21,
the value of it was (0.82) considered an acceptable stability.
Thus, the achievement test has acceptable sincerity and stability.
Table 3. (Table of specifications) for the achievement test
Behavioral
Goals
First
semester

Numberof lessons

RelativeweightRemember

Comprehension

Application

Analysis

Sum

32%

21%

36%

11%

100%

3

2

4

1

10

20

3.2

Personal intelligence scale

Researcher reviewed a number of studies that dealt with multiple intelligences,including interpersonal intelligence to build a scale of personal intelligence consistingof (17) items with five options (strongly agree, agree, neutral, reject, strongly reject)62

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Paper—The Effect of a Proposed Strategy according to the Design Thinking Model in Mathematics…

according to Likert scale, instructions were put in place to answer, they were taken into
account to be clear; to indicate that the results obtained are for scientific research purposes only not used for other purposes. The sample members were asked not to leave
any paragraph unanswered, in case; they are treated as error and write the solution ona piece of paper. Scale was introduced to arbitrators in methods of teaching math.,with psychology, in light of their observations, the modifications were made and they
were approved by 80%. Finding out clarity of scale paragraphs and their understanding by the sample of the exploratory application. Test was applied to (100) students
who are not from the research sample (Al-Mutamayizin High School – Al-Harithiya
for boys) from the research community. It turned out that all instructions were clear;(45) minutes was ok to finish test. Statistical analysis for scale items; the discrimination coefficient was extracted, so the t-test was applied for two independent samples,
because it is an indicator that achieves distinction between paragraphs by comparing
the calculated value with the tabular value (2) with a degree of freedom (58), and a levelof significance (0.05) and it was accepted. Confirming the psychometric properties; thescale presented to a number of experts in math. & Its teaching methods, who agreed
that scale’s paragraphs are appropriate for which they were done for it. So, scale wasapparently valid. The Reliability; He proceeded to extract the stability using the Alpha
Cronbach Equation, its value was (0.79) and considered an acceptable.

4

Results and their interpretation

4.1

Achievement test results

The validity of the first null hypothesis “there is no statistically significant differenceat the significance level (0.05) between the mean scores of the sixth-class biologicalscience students who learned the mathematics subject according to proposed strategy
and students who learned by traditional way in test of achievement test” was tested.
The results were shown in Table 4.
Table 4. “Achievement test”
GroupsNo.

Mean

Variance

Std. Dev.

Exp.

30

70.8

202.49

14.23

Con.

30

62.8

186.32

13.65

t-Test
Cal.

Tab.

2.186

1.97

Indication Level
Statistically significant atthe (0.05) level

From the above table, it’s very clear that (2.186) higher than the tabular one at thesignificance level with degree of freedom (58), which indicates a statistically significant difference in achievement; thus the null hypothesis is rejected and the alternative
hypothesis is accepted. The researcher believes that the reason may be the adoption ofa proposed strategy according to design thinking model, which stimulated the mindsof students to think with the mindset of the designer and search for creative solutions
outside the box, with an emphasis on making the problems presented from the reality ofthe students’ lives, which led to their retention of information for long periods.

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4.2

Results related to personal intelligence test

The null hypothesis “there is no statistically significant difference at the significancelevel (0.05) between both mean scores of the sixth-class biological science students
who learned the mathematics subject assigned to them according to the proposed strategy and between the students who learned by usual way at Personal Intelligence scale”
and it was verified as Table 5.
Table 5. “Personal intelligence scale”
GroupsNo.

Average

Var.

Standard
Deviation

Exp.

30

3.97

0.048

0.22

Con.

30

3.71

0.017

0.13

t-TestCalculated
Value

Tabular
Value

5.21

1.97

Indication Levelsignificant

Noted that the (5.21) is greater than tabular, which indicates existence of statisticallydifference in personal intelligence. Therefore, the null hypothesis is rejected; thehypothesis accepted alternative. The reason may be the adoption of the proposed
strategy according to the design thinking model has affected on personality of thestudents because it allowed them to start thinking freely and this made them realize their
weaknesses and strengths and work on them and thus increase their self-confidence,
also break the barrier of boredom, dryness of mathematics as a subject for them to be
more fun with increased confidence their abilities and personalities.

5

Conclusions

Teaching, according to the proposed strategy, contributed to improving the achievement of students. The use of the proposed strategy contributed to generating positive
impressions in mathematics among students as a new method of teaching. Students
were able to reveal their strengths and weaknesses and face difficulties, thus increasing
their self-confidence. The use of modern teaching strategies has an impact on raising
students’ achievement and improving their personal intelligence.

6

Recommendations

Recommending teachers to adopt the design thinking model in education because of
its role in developing students’ abilities; also paying attention to the personal intelligence(internal and external) of students and paying attention to the importance of making
students confident in themselves and their abilities. Orientation Curriculum Designers in
Ministry of Education to reconsider “Mathematics” curricula for the secondary stage andits content, presenting them in fun and an interesting manner, presenting them in the formof leading students think, research, not to be bound by known methods only, move awayfrom memorization. Conduct training courses for teachers on the design thinking modeland the possibility of suggesting multiple strategies to be applied in the classroom.

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7

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v17i24.35979

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AuthorAreej Khuder Hassan, She is presently Ass. Prof. Dr. and one of the faculty ofcomputer science department, College of Education for Pure Sciences/Ibn Al-Haitham,
University of Baghdad, Baghdad, Iraq. She is interested in methods of teaching mathematics, teaching strategies with multiple Intelligences. She is currently a member ofeditorial board in the peer review process for international journal. The number of articles in national databases – 19. The number of articles in international databases – 10.
E-mail: areej.k.h@ihcoedu.uobaghdad.edu.iq
Article submitted 2022-09-09. Resubmitted 2022-10-27. Final acceptance 2022-10-28. Final version
published as submitted by the authors.

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Paper—The Implementation of Online Learning in Conventional Higher Education Institutions During…

The Implementation of Online Learning in Conventional
Higher Education Institutions During the Spreadof COVID-19: A Comparative Study
https://doi.org/10.3991/ijet.v18i01.36005

Mohd. Elmagzoub Eltahir1,2, Najeh Rajeh Alsalhi1,2,3(*),
Geraldine Torrisi-Steele4, Sami Sulieman Al-Qatawneh5

21College of Humanities and Sciences, Ajman University, Ajman, UAE
Humanities and Social Sciences Research Center (HSSRC), Ajman University, Ajman, UAE
3
Nonlinear Dynamics Research Center (NDRC), Ajman University, Ajman, UAE
4
School of Information and Communication Technology, Griffith University, Australia
5
College of Arts, Humanities, and Social Sciences, University of Sharjah, Sharjah, UAE
n.alsalhi@ajman.ac.ae

Abstract—The purpose of this study is to investigate and explore the degreeof success of the implementation of online learning in conventional higher education institutions instead of face-to-face learning during the spread of the Covid-19
Pandemic during the 2019/2020 academic year, via exploring the undergraduate
students’ perceptions of the application of the online learning system at Ajman
University in UAE, and Griffith University in Australia. In the study, the descriptive approach was used. A questionnaire consisting of 40 items was designed anddistributed to 630 students from Ajman University and 675 students from Griffith
University, who were randomly selected from different faculties of the two universities during the 2019/2020 academic year during the COVID-19 pandemic.
The results of the study revealed that students’ a moderate satisfaction with the
University’s readiness, training, and technical support for online learning and theuniversity’s teaching and learning process during the COVID-19 pandemic, withfemale students finding them more satisfaction than male students. Disciplines
and computer skills also showed an impact on such satisfaction, with Pharmacy& Health Science College students at Ajman University and Architecture, Art,and Design discipline students at Griffith University, and those with excellent
computer skills in both Universities. In addition, the results showed positive attitudes of students towards the use of online learning at the two universities during
the COVID-19 pandemic.
Keywords—online learning, conventional higher education,
COVID-19 pandemic, a comparative study

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1

Introduction

A novel corona virus, known as Covid-19 pandemic, which discovered in the lastmonth of the year 2019, in a seafood market in Wuhan is not only a serious publichealth emergency but an emergency on all societal levels, political, economic, social,and even on education level too [1]. World Health Organization (WHO) announced
social distancing as a means of curbing the spread of this severity pandemic [2]. As aresult of this announcement, schools, colleges, and universities around the world haveclosed down their campuses so that students are taking and follow social distancing
measures [3]. According to the United Nations (UN) report, the pandemic of COVID-19
caused the greatest disruption of education systems in history, affecting approximately
1.6 billion students in more than 190 countries and all oceans. As a consequence of
this severe pandemic, the educational system through their educational institutions has
been motivated to create and develop modern learning strategies to support the continuity of education and training that are appropriate to the environment of this pandemic [4]. Online learning or distance learning has been adopted in most educational
institutions like universities, schools in the world as one of the educational solutions
during the covid-19 pandemic [5, 6]. On another side, although distance learning or
online learning was a widespread matter of concern for political authorities, education,businesses, teachers, parents, and students alike, there was no other alternative. Most
academic heads are now promoting online education as a solution to this crisis [4]. [7]indicated that online learning has become extremely prevalent in a number of higher
education institutions around the world. The biggest international universities over the
past decade are gradually moving their programs online and doing away with traditional learning delivery [8, 9]. Top universities in the world such as Peking University,
Harvard, MIT, Yale, Oxford, Cambridge, among others are moving in this direction[10]. However, moving smoothly from an environment of conventional education to
distance and virtual learning could not happen overnight. This rapid transformation islinked to various obstacles and challenges at this point [11]. For students Lack of properinteraction with instructors is a major concern associated with online learning [12]. It
is important that online learners are familiar with the use of technology to make fulluse of the e-learning system because they will get frustrated when they are not familiar
with the technology and lower their level of satisfaction [13, 14]. In the United ArabEmirates (UAE), 91% of residents use mobile Internet and more than 98% of households have Internet access [15]. In addition, mobile devices, such as smartphones, areused to access the Internet primarily at home or at work (Federal Competitiveness andStatistics Authority). The UAE government through cooperation with both of ministryof education and Etisalat Company implemented online learning during of COVID-19
pandemic from March 2020, and still applied it up to date in all UAE education sectors
like public and private schools and higher education institutions. The main objective of
this measure is to take precautions to protect students from the COVID-19 pandemic
and to ensure students continue to learn in an appropriate manner and with high-quality
teaching methodologies. In order to ensure a successful online learning process duringof COVID-19 pandemic, the Ministry of education in UAE implemented professional

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training for school teachers and allowed private schools to access their own online
learning system. Moreover, It launched smart learning platforms and guidelines andinstructions manual to manage students’ behavior on online learning. In addition, theUAE government offered free satellite broadband services for learners in areas withno connectivity and free home internet for families with no home internet connection.
Ajman University started implementing the online learning system, after joining the
UAE’s efforts to take the necessary precautionary measures to limit the spread of the
new Coronavirus known as (COVID-19) in light of the directives of the Ministry of
Education in the UAE. Moreover, the university organized training courses for morethan 300 faculty members, which included methods and mechanisms for online learning to ensure full readiness for this experiment, and the absence of any challenges
that could hinder the communication process. Furthermore, an interactive environmenthas been provided between students and the instructors that enable them to exchange
views and ideas during the process of explaining educational materials pointing outthat the online learning process is compatible with the skills of the current generationof students in their active dealing. Prior to COVID Griffith university, online learningand blended modes of learning (through the institutional learning management system)
were already present but for the majority of faculty and students, fully online learning
was new. Griffith focused on providing support to both students and staff in online
learning. Numerous online training opportunities were provided to help upskill teaching staff in the use of digital tools.

2

Literature review

2.1

Coronavirus disease (COVID-19) pandemic

A novel strain of respiratory tract infection associated with the COVID-19 emergedin December 2019 in Wuhan at China which is the sprawling capital of Central China’s
Hubei province. This virus has spread rapidly worldwide in all countries of the world,
regardless of whether they are rich or poor countries [1]. WHO On 12 January 2020
isolated this pathogenic virus and named it as the 2019 novel coronavirus [2]. According to [16], Coronavirus (COVID-19) is an enveloped, positive-sense, single-stranded,
RNA virus genome in the size ranging from 26 to 32 kilobases, causes mild to severe
symptoms of acute respiratory syndrome infections and even mortal. Similarly, theMinistry of health and prevention of the UAE defined it as a new strain of coronavirusthat may cause illness in animals or humans. In humans, several coronaviruses are
known to cause respiratory infections ranging from the common cold to more severe
diseases.
2.2

Online learning

Online learning as a model of education emerged in 1982 in California in the unitedstates [17]. This model of education as its first application has had great potential
for effect on the planning, development, and construction of the education system at70

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all educational institutions and its educational levels [18–21]. Allen & Seaman [22]referred that online learning uses computers, laptops, other devices, and the Internet
as the delivery mechanism with at least 80% of the course content delivered online.
Where it is clear that long text-based lectures were not appropriate for the online environment, and learners were not easily and quickly engaged in discussion activities [23].
According to [24], as online learning started to expand, the governments of nations alsobegan funding educational institutions that provided online programs in an improvement in the quality of online learning. Garrison [25], referred that online learningderives from constructivist theory learning, it poses a major change in contrast to conventional distance education, which is focused on the concept of autonomy and theindustrial development of prepackaged study materials. Moreover, according to [26],online learning shifted learning from teacher-centered learning that presented in aclassroom environment during traditional learning to learner-centered learning, wherelearners have much more responsibility and ownership. Where those learners in the
online learning environment are able to choose what to learn when to learn, and who tolearn with [27–29]. Thus, a certain degree of Self-guiding is required to pass an online
class. Online learning, according to [14, 30–32], is a form of distance education inwhich technology mediates the learning process, teaching is provided entirely via theinternet, and learners and teachers are not required to be present at the same time andlocation. Luyt [33] pointed out that features of online learning like access to the internet
and the flexibility of online classes have made online learning an essential componentof the learning system in higher education institutions. Limperos et al. [34] pointed outthat the existence of financial problems in some institutions of higher education andmany students’ demands have directed the focus and shift of these institutions towards
using online learning as one of the propitiate solutions that can be implemented. Furthermore, Al-Qatawneh et al. [35] pointed out that learning nowadays has become reliant on several advanced instructional methods, systems, and multimedia technology,
which have led to the looks of the e-learning platform. Moreover, Online learning has
progressively are becoming prevalent, providing learners flexibility in regard to thetime and location they study, and enabling them to get knowledge rapidly by using
numerous sources of education [36]. Moreover, Chaney [37] referred that nowadays,
the expansion of the use of the Internet and technology have produced a surge in the
demand for internet-based learning. Online learning is attractive to a variety of students
and has become more familiar throughout educational environments including primaryschools to secondary school and into higher education institutions like universities [23].
Moreover, Waldeck [38] pointed out that online learning comprises a wide variety of
programs that use the Internet within and beyond school walls to provide access to
instructional materials as well as facilitate interaction among teachers and students.
Furthermore, according to Garrison [25], in the mid-1990s, advancements in educational technology and increased interest in asynchronous discussion groups gave rise to
the term e-learning, which sought to describe learning delivered entirely online as wellas learning that combines online and face-to-face elements referred to as blended or
hybrid learning. Online learning environments can be classified into three core categories: Asynchronous Online Courses, Synchronous Online Courses, and Hybrid Courses
as seen in Figure 1.

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Fig. 1. Categories of online learning environments

The spread of the COVID-19 pandemic, like so many other aspects of daily life,
has had a major influence on students, teachers, and educational organizations all over
the world [39]. This forced most countries of the world to close their educational institutions [40]. According to [3], this procedure was done so that students could follow
social distancing measures and thus protect them from the risk of contracting a Coronavirus disease (COVID-19). There is uncertainty as to when the pandemic will disappear completely in our lives and the lives of our students. Hence, in effort to maintaincontinuity of learning, most educational institutions around the world decided to takeadvantage of the available technologies, existing resources, and modern means of communication to create online educational programs [41]. Crawford et al confirms thatthe process of shifting smoothly from an environment of traditional learning to online
learning could not happen directly overnight and hence a rapid shift in learning systems will surely be connected to diverse obstacles, problems, and challenges [11]. In
reality, Covid-19 compelled experts and decision-makers in educational institutions
to adopt online learning as a logical choice to address the risk of students ceasing tolearn and study while the Covid-19 Pandemic spread [41]. The rapid shift in learning
system is a test of how well educational institution like universities and schools areprepared to deal with the crisis of pandemic, and how effectively they are able to harness advanced technology, including hardware and software, to enable effective online
learning [42–45]. Oncu and [46] identify the four goals for research to support thesuccess of online learning environments classes (Figure 2).

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Fig. 2. The most important factors in the success of online learning classes

Online learning is considered to hold potential benefits and positive features given
below as seen in Figure 3 [30, 47–49].

Fig. 3. Benefits of online learning [30, 47–50]

However, there are also potential disadvantages refers to the disadvantages of online
learning such as: poor accessibility in Remote Areas, online learning more effectivein digitally advanced countries; lack of access to fast, affordable, and reliable internet
connections, online learning can cause social Isolation, online teachers tend to focus on
theory rather than practice, and online learning is inaccessible to the computer illiterate
population [30, 51].

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2.3

Previous studies

Online learning has been present in higher education for many years. Most institutionsof higher education have been steadily transitioning to online learning, either fully or as
blended models. But, with the advent of the COVID pandemic, the steady transition
turned into a literal dive into online learning. In lockdown online learning changed fromchoice to mandate. Almost overnight, instructors and students around the world wereplunged into a fully online environment, many with little or no preparation or experience. Different educational institutions, in different countries and cultural contexts all
across the globe found themselves to be on the common ground of the challenges of asudden, forced shift to online learning. The situation, though unfortunate, provided aunique opportunity to gain cross-country insights into the experience of online learning,
precipitating a large volume of literature and stimulating international collaboration
among academics. To gain insight into the breadth of literature around online learning
during the pandemic, the authors turned to Scopus. Scopus (https://www.elsevier.com/
en-au/solutions/scopus), lays claim to being the largest database of peer reviewed
literature, and therefore provides adequate insight into the breadth of literature aroundscientific topics of interest, including online learning and teaching during the COVID
2019 pandemic. A brief single ‘surface’ search, in September 2021 of Scopus articles,
abstracts and keywords in articles (using keywords COVID AND learning AND (higher
education), limited to conference papers and journal articles from 2020–2021 yielded1863 relevant documents and the statistics available on Scopus showed that over threequarters of documents were journal articles with almost one-third of articles in thesocial sciences with computer science and medicine being disciplines of particularfocus. The top ‘producers’ of literature were United States, Indonesia and the UK, inthat order. A theme overview of the first 50 relevant papers, by abstract, provides anindication of the focus of the literature. Predictably, there is an emphasis in literature on
the challenges of the Pandemic for higher education institutions and concern for how
the pandemic is reshaping the future of higher education (twenty-eight of the fifty
papers). Investigations of the impact of the pandemic on the student learning/experiences
and faculty experience were also a focal point (nineteen of the 50 papers). Given thecurrent state of higher education where institutions are already being challenged to survive in a global economy and now, even more uncertain economic times, the emphasis
in literature on the institutional challenges and response as well as on the student experience is not surprising. It was observed that exploration of experiences of learning andteaching is occurring mostly within the confines of a particular institution or within aparticular country. Comparative studies across countries were not common with only
two of the 50 most relevant paper. Adding ‘comparative’ to the search terms yielded an
additional five cross-country comparison research but these studies are seemingly
scarce despite the potential richness of knowledge to be gained from what is a ‘global
laboratory [52]. Some studies representative of comparative studies of learning andteaching during COVID 19 are discussed below. Hall et al. [53] write about the impact
of COVID on an international mobile learning project (European DEIMP Project)occurring across six countries including UK, Australia, Belgium, Cyprus, Ireland and
The Netherlands. They explore the strategies used by each country to provide continuityof education across educational systems. One of the key issues arising is that of thedigital divide and its impact particularly on schools and their students. Focusing narrowly74

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on higher education, Oleksiyenko, et al. [54] examine the impact of COVID from theframe of comparative education and international higher education from a global perspective. Tejedor et al. [55] take up the theme of digital literacy and the necessity ofguaranteeing digital literacy for students and teachers to enable higher education tomeet its objectives. The study also considered student perceptions of the competency of
their teachers to teach successfully online, and empowerment of students in the online
classes. Their quantitative study via questionnaire of 376 students across universities in
Spain, Italy and Ecuador showed the necessity of adapting teaching methodologies in
the presence of online learning pointing out that technology is merely a tool and its use
alone does not create effective learning experiences. Tejedor et al. [55] demonstrate thenecessity of training in “competencies within the scope of digital literacy in higher education students” (p. 2) – something that is often taken for granted. The study assessedfrom student perspectives the skill of the teacher to create engaging classes and theusability of teacher’s engaging methodologies (which 56.8% of students found to beengaging and useful. Across Spain, Italy and Ecuador, students in Spain 72.3% perceived
the classes during lockdown to be positive, while 36.95% Ecuador students and 52.8%of students in Italy perceived classes to be positive [55]. Interestingly, aroundthree-quarters of students from Italy and Ecuador students felt teachers had skills appropriate to teaching online while approximately three-quarters of students from Spain feltthat their teachers did not have appropriate skills [55]. The almost global ‘unpreparedness’ of institutions to pivot into online learning during the pandemic is a re-occurring
theme in the surrounding literature. Kummitha, Kolloju, Chittoor and Madepalli [56],add the digital divide as a major concern for institutions. In a cross-sectional study of
281 academics across India and Ethiopia, the authors’ found that lack of institutional
preparedness and especially the digital divide severely limited the effective implementation of online learning. The situation was compounded by a lack of training programs
to help academic staff utilize web resources. However, it is the issue of equitable accessto technologies which is stressed as the most urgent challenge for higher education if
online learning is to be effective and equitable. Furthermore, Zalite and Zvirbule’s [57]study of digital readiness across the European Union pointed to a general lack of digital
literacy adequate to support effective online learning and teaching. They also noticed
the digital gap existing between more developed Nordic European countries and the lessdeveloped Southern and Eastern European countries. Insights into the student experience in the studies revealed that students perceived technological problems as less of anissue than the increased need to take personal responsibility for time management andlearning process. Students’ digital literacy and/or digital readiness as a factor impacting
on learning and the learning experience is taken up by [58]. An k-means cluster analysisof results of 1826 university students revealed significant variation in their digital learning readiness (technology availability, prior experiences and skills). They also foundthat socio-emotional perceptions also play an major role in student learning and thestudent experience during online learning. One interesting backdrop to the findings ofstudies across international borders is the higher education student experience dataobtained from the Student Experience Survey (SES) undertaken by QILT (Quality Indicators for Learning and Teaching) funded by the Australian Government Department of
Education, Skills and Employment. The SES was developed in 2011 as the University
Experience Survey but has since been renamed and expanded to include student from
non-university higher education. The SES ‘paints a picture’ of the student experience

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across the higher education sector in Australia. The 2020 SES included all 41 Australian
Universities and 92 non university higher education institutions. 295, 473 valid surveys
were received, a response rate of 44.1 per cent. In undergraduate students, the quality
rating of their experience fell from 78% in 2019 to 69% in 2020. The greatest decline in
the experience was in relation to learner engagement down 16% to 44% in 2020. It isinteresting that while engagement decreased, participation in discussions either online
or face to face increased marginally from 59% in 2019 to 60% in 2020. Teaching quality
rating was seen to decline to a record low at 78%. The unpreparedness of teachers, students and institutions to pivot to online learning is apparently well documented in literature. In the few cross country studies of learning and teaching online as a result of
COVID clearly show predominantly commonalities the experience and response of students, teachers and institutions to online learning during the pandemic. The dominating
themes are around student, teacher and institutional readiness, the socio-emotional tollof rapid shift to online learning, and the exacerbation of the impact of the digital divide.
2.4

Research problem

Nowadays, the COVID 19 pandemic has had a major impact on many aspects ofdaily life. The education sector is considered to be one of the main aspects affected bythis pandemic, which has forced all the world’s educational institutions into a paradigm
shift in the education system from face-to-face to online learning in the context ofthe pandemic. Implementing online learning effectively can be considered one of the‘wicked problems’ in education. Institutions of higher educations have been delving
into online learning since the advent of the internet in the late 1990’s. The promisethat online learning and new technologies would transform education remains, witha few exceptions, largely unrealized. The forced and rapid shift to online learning asa consequence of COVID-19 is likely to amplify the challenges and opportunities of
online learning and perhaps crystalize new awareness. Hence, it is important to explore
the success of the implementation of online learning in conventional higher education
institutions during COVID-19. As the end-users of online education, insight into theexperiences and perceptions of students are important to gaining greater insights into
how online learning may be improved. Thus, the current study came to verify of degreeof success of the implementation of online learning during a pandemic COVID-19
in higher education institutions in two higher education university, the first is Ajman
University at the United Arab Emirates and the second is the Griffith University at
Australia during the academic year 2019/2020, via exploring the undergraduate students’ perceptions of the application of online learning system at Ajman University and
Griffith University as two examples of higher education institutions in the world.
2.5

Research purpose

With the intent of contributing to an understanding of how quality online learning
can be implemented, the purpose of this study is to explore undergraduate students’
perceptions of the application of the online learning systems at Ajman University in
UAE, and Griffith University in Australia during the spread of the Covid-19 Pandemic
during the 2019/2020 academic year.

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2.6

Research questions

To explore the degree of success of the implementation of online learning during
COVID-19 in two higher education universities, Ajman University at the United ArabEmirates and Griffith University at Australia, the researchers raise the following questions for study:
RQ1: What is the level of student satisfaction with the University’s readiness, training, and technical support for online learning during the COVID-19 pandemic
at Ajman University and Griffith University?
RQ2: What is the level of student satisfaction with the University’s teaching andlearning process at Ajman University and Griffith University during the
COVID-19 pandemic?
RQ3: What are students’ attitudes toward the use of online learning at university
during the COVID-19 pandemic at Ajman University and Griffith University?
RQ4: What are students’ perceptions about development and improvement of Online
Learning at Ajman University and Griffith University?
RQ5: Are there any differences in satisfaction with the use of online learning during
the spread of the COVID-19 pandemic from the perspectives of students at
Ajman University and Griffith University based on gender, disciplines, andcomputer skills?
2.7

The significance of the research

The significance of the study is rationalized as follows:This Investigation– will highlight of students’ perception of the implementation of online learning inconventional higher education institutions during the spread of COVID-19 to solutions that higher education institutions have implemented in the countries of theworld during spread of COVID-19 pandemic.
– may contribute to providing a clearer picture of the difficulties and challenges thatstudents face during implemented of the online learning approach.
– may contribute further insights to the long-standing problem of implementing quality online learning

3

Method3.1

Approach of the study

This study conducted by using the descriptive research approach via a quantitative (questionnaire) research method. According to Nassaji [59], the prime purposeof descriptive research is to examine phenomena and their specific features. Thus, aquestionnaire and Interview instruments will be used to gather data from a sample ofthe population.

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3.2

Study participants

The current study included 630 students from Ajman University and 675 students
from Griffith University, who were randomly selected from different faculties of thetwo universities during the 2019/2020 academic year. during the COVID-19 pandemic.
Tables 1 and 2 shown the Demographic information of Participants and Figures 4 and 5
show the frequency and percentage of participants.
Table 1. Demographic information of participants (Ajman University, UAE)
Study Variables
GenderCollege

Student academic
evaluation (GPA)

Computer Skills

78

Frequency (f)Percentage (%)

FemaleVariables Levels

319

50.63

Male

311

49.37

Total

630

100.00Dentistry

77

12.22

Pharmacy & Health Sciences

63

10.00

Engineering and information Technology

88

13.97

Architecture, Art and design

78

12.38

Business Administration

84

13.33

Law

84

13.33

Mass Communication

54

8.57

Humanities and Sciences

71

11.27

Medicine

31

4.92

Total

630

100.002 – less than 2.5

188

29.84

2.5 – less than 3

244

38.73

3 – less than 3.5

133

21.11

3.5–4

65

10.32

Total

630

100.00

Poor

77

12.22

Moderate

122

19.37

Good

256

40.63

Excellent

175

27.78

Total

630

100.00

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Fig. 4. Participants frequency and percentage (Ajman University, UAE)

Table 2. Demographic information of participants (Griffith University, Australia)
Study Variables
GenderCollegeFrequency (f)Percentage (%)

FemaleVariables Levels

331

49.04

Male

344

50.96

Total

675

100.00School of Medicine

91

13.481

School of Humanities, Languages, andSocial Science92

13.630School of Pharmacy and pharmacology

83

12.296

School of Information andCommunication Technology

97

14.370

Griffith Law School82

12.148

Griffith Business School81

12.000School of Education and Professional
Studies76

11.259School of Dentistry and Oral Health

51

7.556

Architecture, Art, and Design

22

3.259

Total

675

100.00(Continued)

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Table 2. Demographic information of participants (Griffith University, Australia) (Continued)
Study Variables

Student academic
evaluation (GPA)

Computer Skills

Frequency (f)Percentage (%)

2 – less than 2.5

Variables Levels

179

26.52

2.5 – less than 3

276

40.89

3 – less than 3.5

148

21.93

3.5–4

72

10.67

Total

675

100.00

Poor

84

12.44

Moderate

143

21.19

Good

267

39.56

Excellent

181

26.81

Total

675

100.00Fig. 5. Participants frequency and percentage (Griffith University, Australia)

3.3

Study instrument

The questionnaire was used to gather data from the participants students. It was sent
to them during the second semester of the academic year 2019/2020, during the occurrence of the COVID-19 pandemic. The questionnaire comprised of two sections, thefirst section concerned students’ basic information, and the second part represented thequestionnaire elements (n=40) based on the study’s objectives.
The validity of the instrument. A group of arbitrators (10 faculty members of
UAE universities) with extensive experience in the field of education were asked toexpress their views on the items of the questionnaire, in terms of the relevance of
items for achieving the research aims and the number and comprehensiveness of the80

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questionnaire items. The educational specialists’ comments and suggested modifications were taken into account, and relevant deletions, amendments, and additions were
made. As a result, the questionnaire after modification consisted of 27 elements, toachieve the objective of the research.
Reliability of the instrument. To verify the internal consistency of the study tool,
Cronbach’s α method was used. It was applied to a pilot study involving 35 students
from outside the study sample, for which the calculated Cronbach alpha coefficient was
0.776. see Table 3.
Table 3. Cronbach’s alpha coefficients for the reliability of questionnaire domains
DomainNo. of Items

Reliability Coefficientof Alpha Cronbach

University Readiness/Training and Technical Support forOnline Learning

7

0.857

Students Attitudes Towards the Use of Online Learning

20

0.812

Challenges Facing Students in Online Learning

8

0.729

Development and Improvement of Online Learning

5

0.773

All questionnaire

45

0.776

3.4

Data analysis measures

In this analysis, a five-dimensional Likert scale is implemented, as shown in
Table 4 below.
Table 4. Evaluation of scale data based on the options of scale and score intervals
Description
Very highScoresIntervals

5

4.21–5.00

High

4

3.41–4.20

Moderate

3

2.61–3.40

Low

2

1.81–2.60

Very low1

1.00–1.80

3.5

Statistical analysis of the data

For data analysis, the researchers used the Statistical Package for the Social Sciences(SPSS) to compute the percentage, mean, standard deviation (SD), independent t-test
tests, one-way ANOVA, and the Scheffe test.
3.6

Ethical considerations

This study was approved by the Research Ethics Committee/Deanship of Graduate
Studies and Research of Ajman University (Reference number: H-F-H-2020-Oct-21)
on September 26, 2020.

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4

Results

First question: What is the level of student satisfaction with the University’s readiness, training, and technical support for online learning during the COVID-19 pandemic at Ajman University and Griffith University?
To answer the first question of this study, mean scores and standard deviations for the
students’ responses to each of the questionnaire items 1−7 were calculated, as shownin Table 5.
Table 5. Descriptive statistics for the students’ responses to the items about the degree ofsatisfaction with the University’s readiness, training, and technical support for online learning
during the COVID-19 pandemic at Ajman University and Griffith University
No

Items

Q1

Ajman University

Griffith University

Mean

SD

Description

Mean

SD

Description

The university provided me enoughinformation about the online
learning policy

2.76

0.95

Moderate

2.73

0.94

Moderate

Q2

The University has provided me withinformation on how to access anduse the online learning system,(e.g. manuals, videos, email, website)

2.76

1.01

Moderate

2.74

1.02

Moderate

Q3

The information on how to accessand use the online learning systemprovided by the university on online
learning are simple, clear andsufficient

3.12

0.84

Moderate

3.01

0.90

Moderate

Q4

My university provides the online
learning technical support services
that I require

3.07

0.86

Moderate

3.07

0.88

Moderate

Q5

My university provides different
ways to contact the technicalsupport service

2.77

1.02

Moderate

2.72

1.04

Moderate

Q6

The University provides a speedy
response to requests for technicalsupport services

2.71

0.99

Moderate

2.68

1.01

Moderate

Q7

The online learning systems usedby our university are clear and easy
to use

2.91

0.89

High

2.87

0.91

Moderate

Total mean for the dimension

2.87

Standard deviation

0.94

Moderate

2.83

Moderate

0.96

The results presented in Table 5 revealed that the arithmetic mean of all questionnaire items (1–7) for the AU was (2.87), with a standard deviation of (0.94), while for
the GU it was (2.83), with a standard deviation of (0.96). This finding means that student satisfaction regarding readiness, training, and technical support for online learning during the COVID-19 pandemic at Ajman University and Griffith University cameat a Moderate level. Table 5 also shows that the responses of AU students to item 382

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“The information on how to access and use the online learning system provided by theuniversity on online learning are simple, clear and sufficient” had the highest average(3.12), while for GU, item 4 “My university provides the online learning technical support services that I require” was the highest average (3.07), and the two items came inat the Moderate average level. Similarly, a ‘Moderate’ level was also found for Items
4, 7, 5, 2, and 1, of AU with the respective average values of 3.07, 2.91, 2.77, 2.76, and2.76. while for GU, the Item 3, 7, 2, 1, and 5, with the respective average values of 3.01,
2.87, 2.74, 2.73, and 2.72. Furthermore, it is clear from the students’ responses of thetwo universities (AU and GU) in Table 5, that the lowest average (2.71) for AU, and(2.68) for GU was obtained for Item 6 “The University provides a speedy response torequests for technical support services” with also moderate level.
Second question: What is the level of student satisfaction with the University’s
teaching and learning process at Ajman University and Griffith University during the
COVID-19 pandemic?
To answer the second question of this study, mean scores and standard deviations
for the students’ responses to each of the questionnaire items 8−27 were calculated, as
shown in Table 6.
Table 6. Descriptive statistics for the students’ responses to the items about the degree ofstudents’ satisfaction with the University’s teaching and learning process at Ajman University
and Griffith University during the COVID-19 pandemic
Ajman University

Griffith University

No

Items

Mean

SD

Description

Mean

SD

Description

Q8

I am confident I can succeed
academically when learning online

3.23

1.51

Moderate

3.17

1.54

Moderate

Q9

I have the time management/
organizational skills needed tosucceed with online learning

3.23

1.45

Moderate

3.13

1.48

Moderate

Q10

I have the technical skills needed tolearn online

3.02

1.45

Moderate

3.02

1.36

Moderate

Q11

I can get help easily, when I need it,from my instructors/tutors when I
learn online

3.19

1.40

Moderate

3.11

1.43

Moderate

Q12

Using online tools, I can easily
connect with other students, whenlearning online

3.58

1.41

High

3.55

1.43

High

Q13

I like the flexibility offered by online
learning

3.20

1.38

Moderate

3.17

1.39

Moderate

Q14

I prefer to learn online

3.12

1.37

Moderate

3.08

1.37

Moderate

Q15

I prefer to learn on-campus in faceto-face mode.

3.33

1.37

Moderate

3.29

1.38

Moderate

Q16

Learning online is more difficult thanlearning in face-to-face mode

3.64

1.32

High

3.60

1.34

High
(Continued)

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Table 6. Descriptive statistics for the students’ responses to the items about the degree ofstudents’ satisfaction with the University’s teaching and learning process at Ajman University
and Griffith University during the COVID-19 pandemic (Continued)
No

Items

Ajman University

Griffith University

Mean

SD

Description

Mean

SD

Description

Q17

I find getting motivated is moredifficult in online learning than in
face-to-face modes

3.07

1.35

Moderate

2.94

1.45

Moderate

Q18

I find teamwork more difficult online
than in face to face modes

3.55

1.36

High

3.50

1.36

High

Q19

Online education organizes my timein a productive way.

3.53

1.38

High

3.51

1.40

High

Q20

Online learning develops my skills in
the use of IT.

3.48

1.31

High

3.42

1.32

High

Q21

I think that online learning takes into
consideration individual differences
between students

3.77

1.27

High

3.61

2.34

High

Q22

Academic achievement has
improved for me by online learning.

3.43

1.34

High

3.41

1.34

High

Q23

Online learning gives me theopportunity to get back to therecorded lecture at any time.

3.47

1.34

High

3.42

1.36

High

Q24

Online learning promotes students’
self-confidence

3.42

1.33

High

3.43

1.35

High

Q25

Online learning does not provide me
the interaction and discussion I needwith the instructor

3.67

1.35

High

3.66

1.37

High

Q26

I feeling that Using online learning is
comfortable for me.

3.05

1.37

Moderate

3.26

1.45

Moderate

Q27

Online learning increases my social
isolation

3.39

1.23

Moderate

3.74

1.29

High

Total mean for the dimension

3.37

3.35

Standard deviation

1.36

1.44

Moderate

The results presented in Table 6 revealed that the arithmetic mean of all questionnaire
items (8–27) for the AU was (3.37), with a standard deviation of (1.36), while for the
GU it was (3.35), with a standard deviation of (1.44). This finding means that student
satisfaction with the university’s teaching and learning process at Ajman University
and Griffith University during the COVID-19 pandemic is at a Moderate level.
Third question: What are students’ attitudes toward the use of online learning at university during the COVID-19 pandemic at Ajman University and Griffith University?
To answer the third question of this study, mean scores and standard deviations forthe students’ responses to each of the questionnaire items 28−35 were calculated, as
shown in Table 7.

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Table 7. Descriptive statistics for the students’ attitudes toward the use of online learning atuniversity during the COVID-19 pandemic at Ajman University and Griffith University
No

Items

Q28

Ajman University

Griffith University

Mean

SD

Description

Mean

SD

Description

I experience Technical problems
happening during the online classes(network failure or slowness – audio
outage)

3.39

1.30

Moderate

3.40

1.31

Moderate

Q29

Faculty members have weak skills toteach online learning

3.45

1.36

High

3.46

1.37

High

Q30

The weakness of student’s skills inusing online learning.

4.21

1.01

V. High

4.24

1.00

V. High

Q31

Failure and delay of the services oftechnical support.

3.05

1.37

Moderate

3.06

1.40

Moderate

Q32

Students find difficulty in dealing
with online learning assessments.

3.42

1.33

High

3.72

1.15

High

Q33

Online examination systems are
difficult to use

3.67

1.35

High

3.66

1.37

High

Q34

The unsuitability of students’ homeenvironment for participation in
online learning (limited area/presence of children / ....) is
problematic

3.05

1.37

Moderate

3.06

1.40

Moderate

Q35

Online learning reduces the levelof direct communication between
students and faculty members.

3.33

1.37

Moderate

3.61

1.16

High

Total mean for the dimension

3.45

3.53

Standard deviation

1.31

1.27

The results presented in Table 7 revealed that the arithmetic mean of all questionnaire items (28–35) for the AU was (3.45), with a standard deviation of (1.31), while
for the GU it was (3.53), with a standard deviation of (1.27). This finding meansthat the students’ attitudes toward the use of online learning at university during the
COVID-19 pandemic at Ajman University and Griffith University came at a High level.
Table 7 also shows that the responses of AU and GU students to item 30 “The weaknessof student’s skills in using online learning.” had the highest average with the respective
average values of (4.21), and (4.24) and it came in at the High level. Similarly, a ‘High’level was also found for Items 33, 29, and 32, of AU with the respective average valuesof 3.67, 3.45, and 3.42. while for GU, the Item 32, 33, 35, and 29, with the respective
average values of 3.72, 3.66, 3.61, and 3.46. Furthermore, it is clear from the students’
responses of the two universities (AU and GU) in Table 7, that the lowest average(3.05) for AU belong to Item 34, and also Item 34 (3.06) and Item 31 (3.06) for GU was
obtained with also moderate level.
Fourth question: What are students’ perceptions about development and improvement of Online Learning at Ajman University and Griffith University?

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To answer the fourth question of this study, mean scores and standard deviations forthe students’ responses to each of the questionnaire items 36−40 were calculated, as
shown in Table 8.
Table 8. Descriptive statistics for the students’ responses to the items about the development
and improvement of Online Learning at Ajman University and Griffith University
No

Items

Q36

Ajman University

Griffith University

Mean

SD

Description

Mean

SD

Description

There needs to be provision forcontinuous training for different
and updated applications for online
learning.

3.64

1.32

High

4.16

1.19

High

Q37

The necessity to design Courses
Empowered with Videos

3.07

1.35

Moderate

3.02

1.36

Moderate

Q38

There needs to be improvementin providing technical support inquickly and continuously

4.17

1.18

High

3.60

1.34

High

Q39

There needs to be re-organization
and adjustment the assignments,
quizzes, and exams.

3.53

1.38

High

3.51

1.40

High

Q40

The necessity of Creating interactive
content.

3.48

1.31

High

3.42

1.32

High

Total mean for the dimension

3.58

3.54

Standard deviation

1.31

1.32

The results presented in Table 8 revealed that the arithmetic mean of all questionnaire items (36–40) for the AU was (3.58), with a standard deviation of (1.31), while
for the GU it was (3.54), with a standard deviation of (1.32). This finding means that thestudents’ attitudes toward the use of online learning at university during the COVID-19
pandemic at Ajman University and Griffith University came at a High level. Table 8 also
shows that the responses of AU students to item 38 “There needs to be improvement inproviding technical support in quickly and continuously” was the highest mean (4.17),
while for GU, item 36 “There needs to be provision for continuous training for different
and updated applications for online learning.” The highest average (4.16), the two items
came in at the high level. Similarly, a ‘High’ level was also found for Items 36, 39, and40, of AU with the respective average values of 3.64, 3.53, and 3.48. while for GU, theItem 38, 39, and 40, with the respective average values of 3.60, 3.51, and 3.42. Fromthe students’ responses of the two universities (AU and GU) in Table 8, it is clear thatthe lowest average (3.07) for AU belongs to Item 37, as well as Item 37 (3.02) for GU
with a moderate level.
Fifth question: Are there any differences in satisfaction with the use of online learning during the spread of the COVID-19 pandemic from the perspectives of students
at Ajman University and Griffith University based on gender, disciplines, and computer skills? The independent t-test and variance test were conducted to investigate thesignificance of differences between averages. Scheffe’s test for post-hoc comparisons86

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were also conducted to find the significance of differences between means. The results
related to the responses of the study subjects are detailed below according to the study
variables.
Table 9. The means and standard deviations of the students’ responses according to genderN

Mean

Std. Deviation

T. ValueSig (tailed)

AU

University

FemaleGender

311

3.37

1.904

1.904

0.029*

Male

319

3.28

0.6504GU

Female

331

3.37

0.657

1.919

0.028

Male

344

3.27

0.646

Note: *Statistically significant at α ≤ 0.05.

In Table 9, for AU, the calculated t-value of 1.904 was greater than that of the t-table,
indicating significant differences in males and females at the 0.029 significance level,
which is lower than the required level (0.05) which was in favor of female students.
Also, for GU the computed t-value of 1.919 was greater than the t-table, which indicates significant differences in males and females at the significance level of 0.028,
which is less than the required level (0.05), and came also in favor of female students.
Table 10. Analysis of variance according to the disciplines variable by One-Way ANOVA test
Sum ofSquaresAUGU

disciplines

disciplines

df

Mean
SquareF

Sig(tailed)

4.391

0.001

5.007

0.000

Between Groups

14.2918

1.786

Within Groups

252.634

621

0.407

Total

266.926

629

Between Groups

16.2938

2.037

Within Groups

270.917

666

0.407

Total

287.210674

Note: *Statistically significant at α ≤ 0.05.

The findings of the one-way ANOVA test of this variable are shown in Table 10.
As displayed in Table 10, the results clearly illustrated that there are statistically significant differences in students’ perspectives according to the variable of disciplines.
For the AU the p-value is 0.001, which is less than the required statistical significancelevel (0.05). Also, for the GU the p-value is 0.000, which is also less than the required
statistical significance level (0.05). In order to determine the reason for the differences
between the comparisons presented in Table 10, the LSD test was applied. Results fromthe LSD test were showed in the Table 11.

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Dentistry

Medicine(I) College

0.598

0.053140.16321

Law

Architecture, Art, and Design

0.112

0.831

0.02242

0.024

0.183

–.24567*–0.15096

Mass Communication

0.047

Pharmacy & Health Sciences.19785*Engineering & IT

0.086

0.095

0.004

0.892

0.598

Humanities & Sciences

0.17278.27988*.38995*Law

Architecture, Art, and Design–0.22674

0.24916

Medicine

0.037–0.01893

Pharmacy & Health Sciences

Humanities & Sciences

Business Administration

0.070

0.07578

Mass Communication

0.003
0.002

.39952*.42459*Business Administration

0.095

Sig.

Engineering & IT

0.22674

Mean
Difference(I–J)

Dentistry(J) College

Ajman University(J) College

School of
Humanities,
Languages, andSocial Science

Architecture, Art, and Design

School of Dentistry and Oral Health

School of Education and Professional
Studies

Griffith Business School

Griffith Law School

School of Information andCommunication Technology

School of Pharmacy and pharmacology

School of Medicine

Architecture, Art, and Design

School of Dentistry and Oral Health

School of Education and Professional
Studies

Griffith Business School

Griffith Law School

School of Information andCommunication Technology

School of Pharmacy and pharmacology

School of Medicine School of Humanities, Languages, andSocial Science(I) College

Griffith University

Table 11. Results from the Scheffe test were used to determine the causes of differences among students’
responses in relation to the disciplines variable of AU and GU–.29773*

0.01324

0.06678–.25093*.20854*.20284*0.13675–0.06841–0.22932

0.081640.13518–0.18252.27694*.27124*.20515*0.06841

Mean
Difference(I–J)(Continued)

0.050

0.905

0.500

0.010

0.032

0.029

0.157

0.468

0.131

0.465

0.173

0.061

0.004

0.004

0.034

0.468

Sig.

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Engineering& IT

Business
Administration(I) College

0.085
0.137

–.44352*–0.17543–0.14471–0.03464

Pharmacy & Health Sciences

Humanities & Sciences

Law

Architecture, Art, and Design

0.797

0.727

0.000

0.002

–0.02507–.34881*Business Administration

0.047

0.002

0.924

0.225

0.144

0.000

Mass Communication–.19785*

Dentistry–0.00957

Architecture, Art, and Design–.42459*–0.11964

Law

Medicine–0.15036

Humanities & Sciences

0.004–.32374*–.41845*

Mass Communication

Pharmacy & Health Sciences

0.797

0.02507

Engineering & IT

0.003
0.086

–.39952*–0.17278

Sig.

Medicine

Mean
Difference(I–J)

Dentistry(J) College

Ajman University

School ofInformation andCommunication
Technology

School of
Pharmacy andpharmacology(I) College

Architecture, Art, and Design

School of Dentistry and Oral Health

School of Education and Professional
Studies

Griffith Business School

Griffith Law School

School of Pharmacy and pharmacology

School of Humanities, Languages, andSocial Science

School of Medicine

Architecture, Art, and Design

School of Dentistry and Oral Health

School of Education and Professional
Studies

Griffith Business School

Griffith Law School

School of Information andCommunication Technology

School of Humanities, Languages, andSocial Science

School of Medicine(J) College

Griffith University

Table 11. Results from the Scheffe test were used to determine the causes of differences among students’
responses in relation to the disciplines variable of AU and GU (Continued)–.50056*–0.18960–0.13606–.45376*

0.00570–0.06609–.20284*–.27124*–.43447*–0.12351–0.06997–.38767*

0.07179

0.06609–0.13675–.20515*

Mean
Difference
(I–J)

0.001

0.086

0.164

0.000

0.952

0.489

0.029

0.004

0.005

0.277

0.490

0.000

0.470

0.489

0.157

0.034

Sig.

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Pharmacy &Health Sciences

Mass
Communication

0.20410.31417*

Law

Architecture, Art, and Design

0.015
0.005

0.09471.26809*.29881*.40888*Mass Communication

Humanities & Sciences

Law

Architecture, Art, and Design

0.000

0.000

0.424

0.000

.41845*.44352*Business Administration

0.024

0.892

0.006

0.424

0.002

0.004

Engineering & IT

0.018930.17338.24567*–0.09471

Pharmacy & Health Sciences

Humanities & Sciences

Medicine

0.067.34881*Engineering & IT

Dentistry

0.133.32374*

Business Administration

0.183

0.15096

Dentistry

0.598–0.07578

Medicine

Griffith Business
SchoolGriffith Law
SchoolArchitecture, Art, and Design

School of Dentistry and Oral Health

School of Education and Professional
Studies

Griffith Law School

School of Information andCommunication Technology

School of Pharmacy and pharmacology

School of Humanities, Languages, andSocial Science

School of Medicine

Architecture, Art, and Design

School of Dentistry and Oral Health

School of education and Professional
Studies

Griffith Business School

School of Information andCommunication Technology

School of Pharmacy and pharmacology

School of Humanities, Languages, andSocial Science

School of Medicine–0.04680.26416*.31770*.45946*.45376*.38767*.25093*0.18252–.50626*–0.19530–0.14176–.45946*–0.00570–0.07179–.20854*–.27694*(Continued)

0.760

0.021

0.002

0.000

0.000

0.000

0.010

0.061

0.001

0.086

0.163

0.000

0.952

0.470

0.032

0.004

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Law

Humanities &Sciences(I) College

0.14079

Architecture, Art, and Design–.29881*–0.03072
0.11007

Pharmacy & Health Sciences

Architecture, Art, and Design–0.20410

Mass Communication

Humanities & Sciences

0.119640.14471

Business Administration

Engineering & IT–.27988*0.03072–0.05314–.26809*

Pharmacy & Health Sciences

Law

Medicine–0.17338

Mass Communication

Dentistry

0.150360.17543

Business Administration

Engineering & IT

–0.24916–0.02242

Medicine

Mean
Difference(I–J)

Dentistry(J) College

Ajman University

0.273

0.765

0.005

0.067

0.137

0.225

0.598

0.037

0.179

0.765

0.015

0.133

0.085

0.144

0.831

0.070

Sig.

School of Dentistry
and Oral Health

School of
Education andProfessional
Studies(I) College

Architecture, Art, and Design

School of Education and Professional
Studies

Griffith Business School

Griffith Law School

School of Information andCommunication Technology

School of Pharmacy and pharmacology

School of Humanities, Languages, andSocial Science

School of Medicine

Architecture, Art, and Design

School of Dentistry and Oral Health

Griffith Business School

Griffith Law School

School of Information andCommunication Technology

School of Pharmacy and pharmacology

School of Humanities, Languages, andSocial Science

School of Medicine(J) College

Griffith University

Table 11. Results from the Scheffe test were used to determine the causes of differences among students’
responses in relation to the disciplines variable of AU and GU (Continued)–0.31096

0.05354–.26416*0.19530

0.18960

0.12351–0.01324–0.08164–.36450*–0.05354–.31770*

0.141760.13606

0.06997–0.06678–0.13518

Mean
Difference
(I–J)

0.056

0.643

0.021

0.086

0.086

0.277

0.905

0.465

0.019

0.643

0.002

0.163

0.164

0.490

0.500

0.173

Sig.

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The results shown in Table 11 indicate that the source of the differences in the students’ the perspective of students of Ajman University, the differences were related
to the disciplines variable in favor of the Pharmacy & Health Sciences discipline.
While from the perspective of students of Griffith University, the differences wererelated to the disciplines variable in favor of the Architecture, Art, and Design discipline.
Student academic evaluation (GPA). The One-Way ANOVA test results for students’ responses related to the Student academic evaluation (GPA) variable are shownin Table 12.
Table 12. Analysis of variance according to the Student academic evaluation (GPA)
variable by One-Way ANOVA test
AU

GU

ComputerskillsComputerskillsSum of Squares

df

Mean SquareF

Sig (tailed)

Between Groups

2.328

3

0.776

1.836

0.139

Within Groups

264.598

626

0.423

Total

266.926

629

Between Groups

2.904

3

0.968

2.285

0.078

Within Groups

284.305

671

0.424

Total

287.210674

Note: *Statistically significant at α ≤ 0.05.

The results reported in Table 12 indicate that there are not statistically significant
differences in students’ perspectives according to the variable GPA, for both Universities AU and GU.

5

Discussion

The primary purpose of the present study was to gain insights into the level of success of the implementations of online learning during the COVID-19 pandemic in two
institutions, Ajman University in the United Arab Emirates, and Griffith University inAustralia. The surveyed students’ at Ajman University and Griffith University acknowledged positive aspects of online learning amid the disruption of COVID-19. The challenges of online learning are also apparent from student responses to the survey items.
Interestingly, means of the students’ responses to most survey items were comparable
across AU and GU, pointing to similarities in students’ perceptions and experiencesof online learning across both universities – likely a reflection of the similar mannerin which both Universities were forced to plunge, with little time for preparation, into
online learning. When other studies of the online learning experiences of university students during the pandemic are examined, it becomes apparent that despite institutionaland cultural differences across international borders, there are commonalities in student
perceptions and experiences of online learning during COVID-19. The results of thepresent study show that students hold an overall positive view of online learning. The
positive student response of university students to online learning during the pandemic

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is documented in other studies such as [60], Responses to the survey in the present
study indicate student awareness and experiences of some shortcomings in technicalpreparedness of institutions, preparedness of students themselves and the capabilities ofstaff to teach online. The lowest means in the descriptive statistics related to university
readiness, training and technical support are indicative of students’ moderate disagreement that the university provided needed technical information and instructions on how
to use and access online learning, timely technical support, and adequate mechanisms
for contacting support services. Technical preparedness of institution, faculty and students is identified in literature as an important factor to the success of online learning[60]. In the descriptive statistics of the survey items relating to student attitudes towards
online learning there is further evidence of the manifestation of technical challenges of
online learning – students agreed that they experienced technical problems alongside
failure and delay of technical support services. It is clear that technical preparedness(technical support, information, quality of service, training) is an important element of
the online learning experience given that students highly agreed that the provision ofcontinuous training and updated applications for online learning are necessary improvements. More so, students highly agreed on the necessity of providing continuous andtimely technical support to support their online learning. For most universities, prior
to the Pandemic, the trajectory to online learning was characterized by incremental
change, and sometimes fragmented adoption as they sought to shift technology richeducation for social, economic and pedagogical reasons. In the context of having to
unexpectedly respond the circumstance of the Pandemic, the technical preparednessof universities for supporting online learning on mass was put to the test. The needfor better technical infrastructures and timely technical support is brought to the forein other studies of student experiences of online learning during COVI-19 [60, 61].
The preparedness of institutions aside, it is also apparent from the study results thatstudents may have felt themselves to not be as well prepared for online learning as
they needed to be/ or could have been for online learning. They very highly agreed on
the importance of the weakness of students’ skills in using online learning. This finding reinforces that online learning differs substantially from face to face learning andstudents require additional skillsets and capabilities to engage successfully in online
learning. The students surveyed in both universities only moderately agreed that they
had the necessary time management, technical and organizational skills and confidence
to succeed – a finding supported by other studies of student perceptions of online learning during COVID-19 [62]–[67]. Flexibility is an oftentimes stated advantage of online
learning. Surveyed students responded positively to the flexibility and more ‘individualized’ and self-directed capability of online learning, they also acknowledged thechallenges, highly agreeing that motivation, teamwork, connection with others andespecially assessment are difficult in the online environment.

6

Conclusion

The COVID-19 was unexpected and disrupted higher education globally. It provided
a unique opportunity to study online learning across international borders as universities around the world found themselves in very much ‘the same situation’. There’s little

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doubt that the pandemic has not only accelerated the move online learning but amplified the under-developed state of technology rich education and online learning. If weare to truly realize the ‘transformation of education’ that some authors [68–70] claim
as the result of the impact of COVID-19 on education, then we are called to investigate, analyze and learn from the experiences of online learning of institutions on aninternational stage [70–78]. With this in mind, the current study was undertaken andthe survey was constructed to covering a comprehensive range of aspects of the student
experience of online learning during the pandemic across two higher education institutions Ajman University in the UAE and Griffith University in Australia, the study
enriches our understanding of what is important to students in online learning and theresults provide some foundation for formulating some recommendations for practiceand future research. The study has highlighted the comparable experiences of students
in universities across two countries and serves to highlight the broader issues and challenges that universities, regardless of location, are likely to face on the stage of online
learning. To help progress quality online education, some recommendations and considerations arising from the present study are given below in the hope that they will
trigger further discussion and analysis towards advancing high quality online learning.

7

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8

Authors

Mohd. Elmagzoub Eltahir, College of Humanities and Sciences, Ajman University,
Ajman, UAE; Humanities and Social Sciences Research Center (HSSRC), Ajman
University, Ajman, UAE.
Najeh Rajeh Alsalhi, College of Humanities and Sciences, Ajman University,
Ajman, UAE; Humanities and Social Sciences Research Center (HSSRC), Ajman
University, Ajman, UAE; Nonlinear Dynamics Research Center (NDRC), Ajman
University, Ajman, UAE.
Geraldine Torrisi-Steele, School of Information and Communication Technology,
Griffith University, Australia.
Sami Sulieman Al-Qatawneh, College of Arts, Humanities, and Social Sciences,
University of Sharjah, Sharjah, UAE.
Article submitted 2022-08-30. Resubmitted 2022-10-18. Final acceptance 2022-10-21. Final version
published as submitted by the authors.

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

An Analysis of Emotional Responses of Students in
Bilingual Classes and Adjustment Strategies
https://doi.org/10.3991/ijet.v18i01.37125

Suyun Wen()Department of Public Foreign Languages, Shijiazhuang University ofApplied Technology, Shijiazhuang, China
2003110496@sjzpt.edu.cn

Abstract—Students’ willingness to participate in bilingual communication is greatly influenced by their positive emotions in the bilingual class. The
automatic recognition of students’ emotional state in bilingual class can assist
teachers to correctly master the laws of students’ emotional changes during thebilingual learning process as fast as possible. However, the speech emotion features extracted by existing speech emotion recognition models are not universal and not suitable for bilingual speech emotion recognition, and the accuracy
needs to be improved. To cope with these issues, this paper aims to study theemotional responses of students in bilingual class based on emotional analysis
and provide adjustment strategies for it. At first, the signals of students’ speechrecords in bilingual class were pre-processed, the one-dimensional features ofthe signals were converted into 2-dimensional speech spectrum features so as toattain more useful information to facilitate the emotional recognition of students’speech records in the bilingual class. Then this paper combined the bi-linear Convolution Neural Network (CNN) with capsule network to construct a bilingualclass student emotion recognition model, and experimental results verified theeffectiveness of the constructed model.
Keywords—emotional analysis, bilingual class, emotion recognition, bi-linear
convolution neural network (CNN), capsule network

1

Introduction

As positive psychology has been applied in the field of education, now teachers have
paid more attention to students’ positive emotions in the class, especially the bilingual
teachers who need to interact with students more often [1–6]. Studies have shown thatstudents’ willingness to engage in bilingual communication is significantly influenced
by their positive emotions in the bilingual class [7–16]. For this reason, world field
scholars have conducted research on students’ emotional responses in bilingual class,and found that the automatic recognition of students’ emotional state in bilingual classcan assist teachers to correctly master the laws of students’ emotional changes during
the bilingual learning process as fast as possible, thereby formulating targeted teachingintervention measures according to the actual emotional state of students [17–19].

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

Liang et al. [20] pointed out that speech emotion recognition is one of the research
hotspots in artificial intelligence. In the paper, the authors attempted to introduce
speech emotion into classroom teaching and extract the emotional features of speech,
they proposed a multi-channel convolution combining with SEnet network as an emotion recognition model, which performed well in terms accuracy, F1 value, and recall
rate on a self-built emotion dataset. Li et al. [21] proposed a DNN-based multi-modal
learning emotion analysis method, which integrated video and voice to detect the realtime learning emotions of students, the authors applied this method to the automatic
recognition of the learning emotions of students in primary school English class, andused a PAD emotion scale to correspond learning emotions to learning states, thenteachers could judge students’ learning state according to the changes in their learning
emotions and adjust the teaching methods and strategies in time. He et al. [22] useddeep learning and attention mechanism methods to automatically modelling the temporal process, and built a DMTN-BTA model based on multitask learning for recognizing students’ emotions through classroom videos. The proposed method contained a
CNN used for spatio-temporal feature extraction, and a BLSTM-RNN used for emotion
recognition which had introduced a novel bridge-temporal-attention. Lei et al. [23]proposed a deep convolution classroom facial analysis method based on channel interaction for the purpose of solving problems in camera shooting distance, indoor illumination, and other classroom scene factors, and the unbalanced expression image andpoor recognition effect caused by fuzzy facial spectra of students. Their experimental results showed that data enhancement and model improvement have good effects
on facial expression analysis. Liang et al. [24] studied the teacher speech signals anddesigned a set of emotion detection audio processing system to judge their emotions
based on the speech. The authors used the recurrent neural network algorithm to build
a speech emotion recognition classification model, pre-processed the data based on
pre-weighting, frame-adding window, and endpoint detection, re-classified the emotions, and established a speech emotion corpus of teacher evaluation system. Theirexperimental results showed that, the improved eigenvalues of Mel frequency cepstralcoefficients and neural networks can improve the recognition rate of speech emotions
more effectively than conventional speech emotion recognition methods, and can beapplied to speech emotion recognition in classroom teaching.
For bilingual speech emotion recognition, the existing corpus is not individualized
enough, the speech emotion features extracted by existing speech emotion recognition
models are not universal and not suitable for bilingual speech emotion recognition, andthe accuracy needs to be improved. Speech emotion recognition models with excellent
performance are generally improved or build based on fine-tuned conventional models,so choosing the right combination model is an important condition for effective emotion recognition of bilingual speech. This paper studied the emotional responses ofstudents in bilingual class and the adjustment strategies. In the second chapter, thispaper preprocessed the signals of students’ voice records in bilingual class to make thesignals smoother so that the features of the signals could be extracted easier. In the thirdchapter, this paper converted the one-dimensional features of recording signals into
2-dimensional speech spectrum features so as to attain more useful information and to
facilitate the recognition of students’ speech emotions in bilingual class. In the fourthiJET ‒ Vol. 18, No. 01, 2023

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

chapter, this paper combined the bi-linear CNN with capsule network to construct a
combination model for recognizing emotions of students in bilingual class. At last,experimental results verified the effectiveness of the constructed model.

2

Preprocessing of recording signals of bilingual class

During the recording process, there might be various interference factors such asenvironmental noise, equipment noise, speaking interval, low clarity, and irregular
duration in the record database of bilingual class. To make the signals of students’ voicerecords in bilingual class smoother to facilitate feature extraction, the original signalsof bilingual speech records need to be pre-processed.
In the bilingual speech records of students, the energy of high-frequency areas wasless than the energy of the low-frequency areas. This paper used the digital high-passfilter to pre-accentuate the recording signals to increase the signal-to-noise ratio of thebilingual speech records and enhance the energy of the high-frequency areas. Assuming: Y [L] represents the input signal sequence of the bilingual speech records; X [L]represents the output signal sequence, then the formula below gives the formula offiltering processing:
X [L] = Y [L]*0.97Y [L−1](1)

The long-time recording signals need to be truncated, that is, the overlapping area
between a frame and the next frame needs to be separated based on its macro stability,and here the length of the overlapping area is defined as the frame shift. In this paper,
the frame length and frame shift were respectively set as 25 and 10 ms so as to maximize the retained information of student emotions in the bilingual speech records.
The preprocessed recording signals must be continuous, so they need to be superimposed with the window function, namely to be subjected to the windowing operation.
The window function can be considered as a data of equal length, and its expression is
given by the following formula: 2m 
0.54  0.46 cos  , 0  m  M 1
Q ( m)   M 1 
0, Others(2)

To get the frequency spectrum of the signals of the bilingual speech records, the fast
Fourier transform was applied to the recording signals after windowing. Assuming:
B(k) represents the frequency domain sample of the recording signals, b(n) represents
the time domain sample, N represents the size of the fast Fourier transform, expressionof the fast Fourier transform is given by the following formula:B(k ) 

102



N 1
n 0

 2 
i 
 nkb(n) p  N  (k  0, 1, L N  1)

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

3

Feature extraction of speech spectrum of bilingual
record signals

In this paper, the one-dimensional features of the recoding signals were converted
into two-dimensional speech spectrum features to attain more useful information for
the recognition of student emotions in the bilingual class. Compared with conventional CNN, the bi-linear CNN with two parallel convolution layers can perform deep
extraction on the texture features of the spectra corresponding to the recording signals
through the outer product operation of different recording signal features, thereby realizing more satisfactory emotion recognition accuracy. There’re certain similarities in
the features of different recoding signals, to solve the unsatisfactory emotion recognition accuracy caused by such similarities, this paper introduced the capsule network
based on bi-linear CNN.
Generally, the speech spectra are of large size, so this paper converted the large-size
speech spectrum into the Mel spectrogram based on the Meyer filter group to ensurethat the bi-linear CNN could attain speech spectrum input with reasonable size.
Features of the Mel spectrogram could be attained by the following steps:
Step 1: Perform a series of preprocessing operations such as filtering, framing,
windowing, and Fourier transform on the original signals of bilingual speechrecords of students;
Step 2: Calculate the Mel frequency;
Step 3: Perform multiplying and adding operations on the corresponding energy
spectrum to obtain the spectrogram. Some research results have verified that theMel frequency is more consistent with the auditory characteristics of human,
namely the two have a basically linear relationship, its expression is given by
Formula 4:g 
g MF  2595 1g 1 
 700 Hz 

(4)

Formula 5 gives the expression for converting into actual frequency: nNS 1 (n)  700  p 1125  1



(5)

Assuming: L represents the length of FFT, Gr represents the sampling rate, Γ() represents the integer function, the formula below calculates the Mel frequency resolution:
 ( L  1) * f (i ) g (i )   

Gr



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Assuming: n represents the n-th Mel filter; g(n–1) and g(n+1) represent the upper
frequency and lower frequency of the filter; g(n) represents the center frequency of the
Mel filter; l represents the serial number of dots; multiple filters are defined and denoted
as Fn(l), the formula for calculating the output of the filter is: l  g (n  1) g (n)  g (n  1) , g (n  1)  l  g (n) l  g (n  1), g (n)  l  g (n  1)Fn (l )  
 g (n)  g (n  1)0, Others(7)

Assuming: |a(l)|2 represents the size of the energy of the l-th dot in the energy spectrum, the Mel spectrogram was attained by multiplying and adding the energy spectrumwith the filter output corresponding to each dot, the calculation formula of the Mel
spectrogram is given by the following formula:
MclSpec(n) g ( n 1)

 F (l ) * A(l )l  g ( n 1)

4

2

(8)

n

Construction of the bilingual class student emotion
recognition model

In this paper, bi-linear CNN was adopted to extract the fine-grained features of thespectra corresponding to the recording signals of student speech in the bilingual class,the effect of spectrum texture recognition was good, which can be used for the emotion classification and recognition of different language speech signals. Figure 1 gives
the structure of bi-linear CNN. Assuming: gx and gy represent the feature functions
extracted by different convolution kernels; E represents the pooling operation; D represents the speech signal emotion classification function, then the bi-linear CNN model
can be written as G=(gx, gy, E, D).
Feature function g(h) can map spectrum F corresponding to the input recording signals and its position K to into a feature with a size of d×C, which satisfies the function
mapping relationship of g:K×F →Rd×C. In order to attain the effective bi-linear convolution features, the different features of spectra corresponding to the input recording
signals at a same position were combined through the outer product operation of thematrix. Assuming: Y represents the bi-linear operation, then there is:Y(k, I, gx, gy ) = gx (k, I )T gy(k, I )

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

Fig. 1. Structure of bi-linear CNN

Because for different types of languages, there will be large differences in theexpression of a same emotion, so when recognizing the emotions in the speech signals
containing two kinds of languages, there might be differences in the extracted featuresin terms of size, direction, and state. Although the convolution operation of CNN can
extract more subtle matrix features based on the attained emotion features of recording
signals, it cannot deal with the changes in above features, and the emotion recognition
effect will be greatly affected, so in order to make up for the defect of CNN, this paper
introduced the capsule network.
Capsules in the capsule network transmit in the form of vectors, the probability andattribute of the emotion features of recording signals are respectively characterized
by the length and dimension values of the vectors. For the recording signals being
detected, if the corresponding spectrum and its position or state undergoes changes,
then it can be considered that only the vector direction has changed, and the vector
length hasn’t changed.
Assuming: qi represents the transition matrix, then the vector of the extracted finegrained features of the spectrum corresponding to the recoding signals was multipliedby qi to get the vector that can be recognized by the capsules in the capsule network,
the formula is:vi = qi xi

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Different capsule vectors need to be assigned with different weights, the expressionof weight di is given by the following formula:di = softmax ( yi )(11)

The weighted vector r can be expressed as:
r

d v

(12)

i i

i

Because the value range of the probability of emotion features of recording signals is[0,1], this paper introduced a squashing function to compress the capsule vector lengththat represents this parameter, and the formula is:
ui 

r

2

1 r

2

r
r

(13)

Fig. 2. Flow of the dynamic routing algorithm

The calculation results of the above formula were updated based on the dynamic
routing algorithm. Figure 2 shows the flow of the dynamic routing algorithm. If thealgorithm terminates, then the final result ui is output; if the algorithm does not terminate, then yi is updated, and the algorithm enters the next-round iteration, the following
formula gives the calculation formula of yi:

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

yi* = yi + viui

(14)

When a certain emotion feature appears in the feature spectrum corresponding to therecording signals, then the final output of the capsule network is an instantiated vector
that represents the probability of the occurrence of the emotion feature, and the lengthof the vector should be relatively long. The following formula gives the expression ofedge loss function Kl used for student emotion recognition:

K l  Ol max 0, n   u2   (1  O )max  0, u  n 
l



2

(15)

If a student emotion is recognized as the l-th type, then Ol=1; otherwise, Ol=0. n+
and n– were used to limit the length of the vector, and their values were 0.9 and 0.1. Inorder to reduce the loss of recognition when the student emotion type does not exist,
μ was set to 0.5.
After the speech spectrum features corresponding to the recoding signals of students
in bilingual class were constructed, this paper combined the bi-linear CNN with capsule network to construct a combination model, the bilingual class student emotion recognition model, as shown in Figure 3, the bi-linear CNN can further attain the featuresof the speech spectrum textures corresponding to the recoding signals to improve theaccuracy of emotion recognition, and the capsule network had made up for the defectthat the CNN cannot deal with the changes of features in size, direction, and state.

Fig. 3. Structure of the combination model of bilingual class student emotion recognition

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The features of the Mel spectrogram corresponding to the recording signals of student speech in bilingual class and the features of statistic were fused to generate the
combined features with time series attribute. The two parallel paths in the bi-linear
CNN then performed convolution and pooling operations on the combined features for
multiple times, and the deep-level features of the recording signals were attained. Next,
after the outer product processing, the deep-level features were input into the capsule
network, and eigenvectors representing the probability and attribute of the emotion
features of the recording signals were judged to attain the ultimate correct recognition
results of the emotions of students in the bilingual class.
The fusion of the features of the Mel spectrogram corresponding to the recording
signals of student speech in bilingual class and the features of statistic has a large
impact on the quality of the extracted features. The specific steps of feature fusion are:
Step 1: The recording signals were converted into a Mel spectrogram feature matrix
with a size of m*n, wherein m and n are respectively the frequency length andtime length of the matrix.
Step 2: The features of the frame-statistic of the recoding signals were extracted toattain a m1*n feature matrix, wherein m1 and n respectively correspond to thefeature number and time length.
The feature matrix of the Mel spectrogram and the feature matrix of the statistic werefused according to time series to generate a combined feature matrix with a dimensionof (m+m1)*n. Assuming: rij and Kij respectively represent the vector of the j-th segmentof the i-th Mel spectrogram and the i-th frame-statistic feature, then there is:wij = [rij, Kij]

5

(16)

Experimental results and analysis(a) Training set(b) Test set

Fig. 4. Distribution of emotion type samples of the training set and test set

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies

Proportions of the emotions in the training sample set and test sample set of therecording signals of students in the bilingual class are given in Figure 4a and b, theemotions contain five types: cheerful, relaxed, regretful, excited, and nervous.
Table 1. Student emotion recognition results of different networks
Emotion Recognition
Model

IPL Features

Mel SpectrogramFeatures

Statistic
Features

Combined
Features

VGG16

75.62%

71.92%

71.6%

75.4%

Resnet34

70.9%

71.4%

77.9%

80.9%

SVM

74.6%

77.3%

70.7%

76.2%

LSTM

79.3%

76.9%

73.4%

73.4%

Residual network

85.7%

83.1%

88.7%

85.9%

The proposed model

80.4%

87.4%

89.4%

92.7%

Table 2. Confusion matrix of student emotion recognition numbers
corresponding to Mel spectrogram features
Cheerful

RelaxedRegretful

Excited

NervousCheerful

36

2

1

4

7

Relaxed

2

25

8

3

5

Regretful

4

1

17

2

4

Excited

7

2

6

28

6

Nervous

2

9

1

6

29

Table 3. Confusion matrix of student emotion recognition numbers
corresponding to combined features
Cheerful

RelaxedRegretful

Excited

NervousCheerful

36

2

7

4

5

Relaxed

6

22

9

5

1

Regretful

8

5

28

3

7

Excited

3

8

6

29

4

Nervous

8

4

2

1

26

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The final student remotion recognition results are shown in Table 1. According to
the table, compared with other five types of emotion recognition models, the proposedmodel that fused the features of the Mel spectrogram and the frame-statistic exhibited
better recognition effect, and it can further improve the accuracy of emotion recognition of different students in the bilingual classroom environment.
To further verify the effectiveness of the feature fusion processing of the Mel spectrogram and the frame-statistic, this paper designed comparative experiments, andTables 2 and 3 respectively give the confusion matrix of student emotion recognition
numbers corresponding to Mel spectrogram features, and the confusion matrix of student emotion recognition numbers corresponding to combined features. By comparing
the two tables, it’s known that the combined features can effectively and correctly classify the five types of student emotions in the bilingual class. More intuitively, according
to the comparison of student emotion recognition rate for different features shown in
Figure 5, the combined features had improved the recognition rate of the five types ofstudent emotions to a certain extent.

Fig. 5. Comparison of student emotion recognition rate for different features

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Paper—An Analysis of Emotional Responses of Students in Bilingual Classes and Adjustment Strategies(a) English(b) English+Chinese(c) German+Chinese

Fig. 6. Confusion matrixes of speech emotion recognition model
under monolingual and bilingual conditions

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To verify the applicability of the bilingual class student emotion recognition model
constructed in this paper to different language samples, this paper took the English,
English+Chinese, and German+Chinese classes as examples to carry out related experiments, and Figure 6 gives the experimental results. By observing the three confusion
matrixes corresponding to English, English+Chinese, and German+Chinese classes, we
can know that, based on the extracted features of bilingual speech signal spectra, theproposed model achieved ideal recognition rate of student emotions, that is, the model
can well capture the features of emotion factors that are not significant enough, which
had further verified the effectiveness of the proposed model. Based on the output of thismodel, teachers can adjust their teaching strategies in time to promote the interaction
between teachers and students in bilingual class.

6

Conclusion

This paper studied emotional responses of students in bilingual class based onemotional analysis and the adjustment strategies. At first, this paper preprocessed therecording signals of bilingual class and converted the one-dimensional features of therecording signals into 2-dimensional speech spectrum features to attain more useful
information and to facilitate the recognition of student emotions in the bilingual class.
Then, this paper combined the bi-linear CNN with capsule network to construct a combination model for recognizing emotions of students in the bilingual class. After that,the experimental results of student emotion recognition of different networks provedthat the proposed model outperformed other five emotion recognition models in termsof recognition effect. The confusion matrix of student emotion recognition numbers
corresponding to Mel spectrogram features, and the confusion matrix of student emotion recognition numbers corresponding to combined features were given, which had
further verified the effectiveness of the feature fusion processing of the Mel spectrogram and the frame-statistic. At last, the confusion matrixes of speech emotion recognition model under monolingual and bilingual conditions were given, which had verified
the applicability of the bilingual class student emotion recognition model constructedin this paper to different language samples.

7

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labour and burnout among further and adult education teachers in the U.K. International
Journal of Emerging Technologies in Learning, 15(24): 26–37. https://doi.org/10.3991/ijet.
v15i24.19305[2] Walifa, R.K. (2020). The influence of policy on emotional labour and burnout among further
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Traitement du Signal, 38(3): 865–873. https://doi.org/10.1134/S105466182103024X

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39(2): 651–658. https://doi.org/10.18280/ts.390228[5] Zhang, Z., Li, Y., Sun, S., Tang, Z. (2022). Intervention effect of group counseling based on
positive psychology on psychological crisis of college student. Computational Intelligence
and Neuroscience, 2022: 3132016. https://doi.org/10.1155/2022/3132016[6] Jiang, L., Zhang, Y. (2022). Prediction and influencing factors of big data on college students’ positive psychological quality under mobile wireless network. Mobile Information
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China, pp. 421–425. https://doi.org/10.1007/978-3-030-77417-2_34[8] Guo, J., Cong, X., Miao, Z., Feng, Z., Yang, E. (2021). Research and practice on bilingual
teaching method of applied talents training based on flipped classroom teaching mode. In
2021 2nd International Conference on Big Data and Informatization Education (ICBDIE),
Hangzhou, China, pp. 591–594. https://doi.org/10.1109/ICBDIE52740.2021.00140[9] Palacios-Hidalgo, F.J. (2020). Video-based analysis of pre-service primary bilingual teachers’ perceptions about the inclusion of gender and LGBT+ issues in the EFL classroom. In
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pp. 45–48. https://doi.org/10.1145/3341042.3341067[11] Suárez, E., Otero, V. (2014). Leveraging the cultural practices of science for making classroom discourse accessible to emerging bilingual students. Boulder, CO: International Society of the Learning Sciences, 2: 800–807. https://doi.dx.org/10.22318/icls2014.800[12] Ren, X., Han, J., Hu, D. (2011, August). Reform and Practice on Bilingual Classroom
Instruction Patterns in Professional Courses Based on Web Resources. In International Conference on Computer Science, Environment, Ecoinformatics, and Education, Wuhan, China,
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org/10.1016/j.chb.2015.12.056[15] Bovo, R., Callegari, E. (2009). Effects of classroom noise on the speech perception of bilingual children learning in their second language: Preliminary results. Audiological Medicine,7(4): 226–232. https://doi.org/10.3109/16513860903189499[16] Bourguet, M.L. (2006). Introducing strong forms of bilingual education in the mainstream
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org/10.1088/1742-6596/1651/1/012158

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Networks, 22(S4): 2146011. https://doi.org/10.1142/S0219265921460117[22] He, J., Peng, L., Sun, B., Yu, L., Guo, M. (2021). Dual multi-task network with bridgetemporal-attention for student emotion recognition via classroom video. In 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, pp. 1–8. https://doi.
org/10.1109/IJCNN52387.2021.9533471[23] Lei, L., He, Y., Li, X., Yu, S., Yin, Y., Qin, L., Liang, M. (2021). Classroom facial emotion
recognition based on channel attent. In 2021 IEEE International Conference on Emergency
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org/10.1109/ICESIT53460.2021.9697038[24] Liang, J., Zhao, X., Zhang, Z. (2020). Speech emotion recognition of teachers in classroom teaching. In 2020 Chinese Control and Decision Conference (CCDC), Hefei, China,
pp. 5045–5050. https://doi.org/10.1109/CCDC49329.2020.9164823

8

AuthorSuyun Wen, graduated from School of Foreign Studies, Hebei Normal University
with a master’s degree, is currently working in the Department of Public Foreign Languages, Shijiazhuang University of Applied Technology with a research direction ofEnglish education.
Article submitted 2022-11-03. Resubmitted 2022-12-09. Final acceptance 2022-12-11. Final version
published as submitted by the authors.

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Paper—Development of the Hands-free AI Speaker System Supporting Hands-on Science Laboratory…

Development of the Hands-free AI Speaker
System Supporting Hands-on Science
Laboratory Class: A Rapid Prototyping
https://doi.org/10.3991/ijet.v18i01.34843

Gyeong-Geon Lee, Minji Choi, Taesoo An, Seonyeong Mun, Hun-Gi Hong()Seoul National University, Seoul, Republic of Korea
hghong@snu.ac.kr

Abstract—The recent progress of natural language processing (NLP), speech
recognition, and speech generation envision using hands-free artificial intelligence (AI) speakers in classrooms to support student learning. In science education, conventional hands-on laboratory education has been considered crucial
in fostering students’ manipulative experimentation skills. However, touching
things with gloved hands other than experimental equipment and apparatuses isstrictly restricted because of the safety issue, which calls for another channel to
get timely support. Therefore, we ideated that adopting hands-free AI speakers in
the hands-on science laboratory classroom would support student learning. Using
the rapid prototyping method, we designed and developed an AI speaker-based
system that answers student queries concerning solution-making, experimental processes, and waste liquid disposal, corresponding to the initial, middle,
and final phases of a laboratory class. The system was internally validated byusability tests of 9 expert panels and 18 university students and then revised.
The revised system was externally validated in an analytical chemistry experiment class for 3 sessions with 13 university students. We present the result of the
prototype development and internal and external validations with quantitativeand qualitative data. The AI speaker system enabled students to use the auditory
learning mode in the laboratory while concentrating on the experimentation with
their hands in the external validation. Future research topics were suggested.
Keywords—AI in education (AIEd), hands-free AI speaker, hands-on sciencelaboratory class, rapid prototyping, natural language processing (NLP)

1Introduction & background

The innovative educational research and practices to follow up the rapidly changing
contemporary world are now supported by Artificial Intelligence (AI) technologies [1].
The Natural Language Processing (NLP) technology has explosively grown due to thedeep learning algorithms [2], which are relevant to converting human speech to text
(STT; or speech recognition, SR) and text to human-like speech (text to speech, TTS;
or speech generation). This technological development yielded products, services, andiJET ‒ Vol. 18, No. 01, 2023

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platforms of AI-based smart speakers, such as Microsoft Cortana, Apple Siri, Amazon
Alexa and Google Home [3]; and they are more and more integrated into our daily lives.
For decades, scholars have insisted that the AI in Education (AIEd) will bring innovations in teaching and learning and suggested their framework for its roles [4–6].
Although there can be many frameworks of visions for AIEd, its epitome seems clear:
the AI comes into the classroom where there are instructors and students, changes theprocesses of learning, reducing instructors’ burdens for repetitive work and allowing
them to engage with more valuable teaching behavior, and supporting student learningin cognitive, affective, or any other learning modes (based on [6–8]).
However, there seems that there has not been much empirical research on the possibilities of AI Speakers in Education (AISEd), and there is still a need for explorative
studies on its utility in being an intelligent agent to answer students’ questions based onnatural languages. (cf. [9]) Some innovative research on AISEd has focused on the possibility of native language or English education [6, 10–11]. Nevertheless, it seems that
there has been little research that incorporated AI speakers into the science classroom.
Science education has emphasized the ‘hands-on’ laboratories science 1980s [12].
In the usual hands-on science laboratory class, students gather weekly in the lab and conduct an experiment to get data, which is interpreted in light of scientific theories. And theimportant learning mode in the so-called hands-on laboratory class is kinesthetic [13] asthey are expected to foster experimentation skills [14–15]. Students manipulate apparatuses and equipment such as beakers and other glassware, pipettes, pH meter, UV-VIS
spectrometers, and treat reagents that need caution to avoid accidents [16]. Therefore,
strict safety rules compel students to wear latex gloves, goggle, and lab coats and notallow them to touch non-experimental items with gloved hands, such as door handles,
elevator buttons, smartphones, laptops and even lab notes (see [17]).
Consequently, in principle, students cannot freely access the information they needduring the experiment and cannot help but ask TA frequently. Here, the ‘hands-free’
characteristic of the AI speaker stands out – as a user need not use their hands to operate
it but verbally calls a system to get the information they require, students in a sciencelaboratory class will also benefit from it, without mitigating the safety rules. Therefore,
the need for empirical research that implements AI speakers in an authentic laboratory
teaching and learning site arise.
In this study, the researchers took the design and development (D&D) research
approach for the hands-free AI speaker system supporting a hands-on science laboratory class. As the product of this research is a novel instructional tool, the rapid prototyping (RP) method that acquires user feedback for revision before the final fieldtest would be helpful. Throughout the development and evaluation of the AI speakersystem, the researchers would investigate its merits based on the responses of experts
in AI, educational technology, and science education, and university students. And further, the current status, limitations, and future directions for the AI speaker system inthe science laboratory class will be discussed.

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2

Research questions

1. How the prototype of the hands-free AI speaker system supporting a hands-on
science laboratory class is developed using the RP approach?
2. How was the result of the internal validation of the prototype of the hands-free AI
speaker system supporting a hands-on science laboratory class?
2.1. What were the responses from experts?
2.2. What were the responses from university students?
3. How was the result of the external validation of the prototype of the hands-free
AI speaker system supporting a hands-on science laboratory class? (what were theresponses from university students who experienced the system in the authentic science laboratory classroom?)

3

Method

3.1

Design and development research

D&D research is “the systematic study of design, development and evaluation processes with the aim of establishing an empirical basis for the creation of instructional …
tools” [18]. D&D research is categorized as follows – Type I: Product & tool research
and Type II: Model research [18]. As the former is relevant to this study, we focused on
the specific product and yields context-specific lessons learned from developing it andanalyzing the conditions for its optimal use [19].
3.2

Rapid prototyping approachThis study took an RP approach [20–21]) to develop a hands-free AI speaker system
supporting hands-on laboratory classes. As the RP involves the typical stages of analysis, design, development, implementation, and evaluation (ADDIE) while emphasizing
the formative evaluation and iterative process [22], we followed the generic steps.
3.3

Research field

The research field of this study was Hankuk University (pseudonym), located inSeoul, Republic of Korea. Two considerations arose from the characteristics of theresearch field: (1) As the STT technology of the Korean language is behind that of
the English language, the performance of the AI speaker also reflects its current level.
(2) Most student participants in this study were enrolled in the Department of Chemistry
Education – i.e., most of them were pre-service chemistry teachers.
3.4

The ADDIE process

Need analysis and design of the AI speaker system. The researchers of this study
consisted of one expert, one doctoral candidate, and three master’s students of chemistry

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education. They all had a teacher’s license for secondary chemistry. They had years ofuniversity laboratory teaching experience.
As a need analysis, the researchers reviewed literature related to the characteristicsof hands-on science laboratory education (e.g. [12, 14–15]) to analyze the need for the
AI speaker system for hands-on laboratory classes (cf. [23]). And they preliminarily
asked several pre-service chemistry teachers what functions of the AI speaker system
would help them take laboratory classes. As a result, the researchers concluded thata supporting tool for hands-on science laboratory class should reduce the instructors’
repeated work and promote student learning, particularly for necessary but repetitive
work in the laboratory class – i.e., solution-making, checking experimental procedures
and disposing of liquid waste according to safety rules. The three functions of the AI
speaker system derived from those needs are presented in Table 1. And the overall conceptual flow chart for the system is illustrated in Figure 1.
Table 1. Summary of three functions of hands-free AI speakersystem supporting hands-on science laboratory class
Target Phase of theLaboratory Class

Function

Need

References(1) Solution-making
helper

The beginning– Students must calculate the amount ofreagent needed to make various kinds ofsolutions using complex formulas.

[24–27](2) Experimental
process helper

The middle– Students repeatedly check that they
are proceeding well according to theexperimental procedure.
– Students frequently question the reason
and issues of a certain procedure.

[28–30](3) Liquid waste
disposal helper

The end– Students must dispose of liquid wastes
into each safety container according totheir property.

[31–33]Fig. 1. Conceptual flow chart (scenario) of the AI speakersystem supporting hands-on laboratory118

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Paper—Development of the Hands-free AI Speaker System Supporting Hands-on Science Laboratory…

The NUGU platform required the AI speaker to be connected online to receive asignal from the servers. Therefore, it was considered that the AI speaker should be connected to the Wi-Fi as a network environment. If the Wi-Fi speed increases, the timerequired to SR, NLP, and respond to user articulation would decrease. Therefore, thenetwork environment had to be accessible to high-speed Wi-Fi.
Meanwhile, the user-AI speaker interface was designed. As most AI speaker platform
provides a Q&A system with alternate articulation between the user and the speaker,
this study followed the structure. The three functions were initially designed to havea single-turn conversation – i.e., the user asks a speaker a question in a sentence andthe speaker answers for that. While designing and testing function 1, the need for error
handling arose while the function needed to recognize three parameters from the user’s
speech. Therefore, the researchers took a multi-turn approach to recognize a parameterfrom the user’s single articulation.
Development of the prototype. We developed the prototype of the AI speaker system upon the NUGU AI speaker platform serviced by SK Telecom Co., Ltd. In theplatform, entity means a developer-defined category of word tokens; intent implies theuser’s intention while speaking to the AI speaker, inferred by a combination of entities;
and action means the AI speaker’s response matched with each intention. We defined
the required entities, intents, and actions in the NUGU Player Builder (Figure 2). The
examples of entity, intent, and action for each function are presented in Figure 3. And
we trained the natural language understanding (NLU) model in the NUGU platform byinputting several word tokens for an entity and dozens of sentences that include a combination of entities for an intent. The actions of function 2 and function 3 were directly
saved in the Play. Meanwhile, the external proxy server was connected to function 1 in
the JSON format using Python Flask to calculate the amount of reagent.

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Fig. 2. Example of the development on the NUGU play builder

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Fig. 3. The corresponding structure of user intents, entities, and actions in the AI speaker system

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Internal validation as a formative evaluation. The researchers shot a video thatshows a person using the AI speaker prototype with three functions in sequence. In thevideo, a person awakes the system and asks the speaker how to make a specific solution(function 1), what is the first or second step of the experimental procedure (function 2),
and where to dispose of a couple of kinds of waste liquids (function 3), and finally ends
the session. The video was about 2 minutes long.
The participants of the internal validation consisted of two groups: the expert
review panel and the student group. The expert review panel consisted of nine experts
from three fields – three from AI technology, three from educational technology, andthree from science education. They were asked to read the documents related to thebackground and process of this study, watch the video, and respond to an individualinterview, explicating their thoughts on the system. The student group consisted of 16
pre-service chemistry teachers, 1 in-service chemistry teacher, and 1 chemistry graduate student. Ten of the pre-service teachers were taking an Analytical Chemistry Experiment (ACE) class for the secondary pre-service chemistry teachers.
The items for the usability test were adopted from the PACMAD (People At theCentre of Mobile Application Development) usability model [34]. Further, we asked
them about the pros and cons of the system and suggestions for the system revision.
External validation as a summative evaluation. We implemented the hands-free
AI speaker system supporting a hands-on science laboratory class. The research field
was the ACE class at Hankuk University mentioned above. Thirteen university students(pre-service chemistry teachers) who took the course participated in the study.
While revising the AI speaker system based on the results of the internal validation,
the researchers implemented and observed students using each function of the system
in a laboratory session (Table 2). The scene of students using the AI speaker-basedlaboratory class supporting system was videotaped.
Table 2. Functions of the hands-free AI speakersystem supporting hands-on science laboratory class
SessionContent of Experiment

Tested Function

1

Production and standardization of KMnO4 solution

Solution-making helper

2

Titration in the non-aqueous solvent

Experimental process helper

3

Iodiometric titration of Vitamin C

Liquid waste disposal helper

After the three sessions with the AI speaker system, students were asked to respondto the survey. The survey consisted of three parts: (1) The system usability scale (SUS)
consisted of 10 items on a 5-point Likert scale [35]. (2) The researchers newly developed a survey to investigate students’ Perceptions of an AI Speaker System in a Science
Laboratory Classroom (PASS-SLC) that consisted of 18 items on a 5-point Likert scale.
It was developed with reference to the previous literature on the technology acceptancemodel or usability of AI speakers or chatbots [36–39], and the learning outcomes ofthe science laboratory class [14–15]. (3) The survey included open-ended questions at
the end of SUS and PASS-SLC, asking, “why did you think like that for these items?”
And the survey asked students about the pros and cons of the AI speaker system andsuggestions for future revision.

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4

Results

4.1

The prototype of the AI speaker system supporting hands-on sciencelaboratory class

Figure 4 represents the one-shot demonstration video of the prototype of the AI
speaker system developed in this study, which is about 2 minutes long. At first, a
researcher awakened the system by calling ‘Aria’ (the designated name of the agent
in the NUGU platform) (Figure 4a and b). After the Play was loaded, a researcher
requested function 1 to function 3 in sequence, which the speaker successfully performed (Figure 4c–v). Note that, during the multi-turn conversation in function 1, thespeaker handled an error recognizing the VOLUME entity (Figure 3), asking the user
again (Figure 4h–k). After the user finished testing all the functions, he commanded to
shut down, which was realized (Figure 4w–x). The response time of the AI speaker foreach articulation of the user took a couple of seconds.
4.2

Internal validation

The survey scores from the expert review panel and university students are presented
in Table 3.
Table 3. The PACMAD quantitative survey result of internal validation (1–4 scale)Category
User

Task

Content

Expert (N = 9)Student (N = 18)

Effectiveness

Item

3.55 (.53)

3.29 (.69)

Efficiency

3.44 (.73)

2.88 (.86)

Average

3.5 (.56)

3.09 (.59)

Satisfaction

3.22 (.67)

3.18 (.57)

Learnability

3.67 (.71)

3.65 (.61)

Average

3.44 (.58)

3.41 (.57)

Memorability

3.89 (.33)

3.59 (.62)

Error

3 (.5)

2.76 (.75)

Cognitive load

3.78 (.5)

3.44 (.63)

Average

3.56 (.29)

3.26 (.44)

3.51 (.3)

3.25 (.43)

Overall

Expert review panel. Experts evaluated the effectiveness of the prototype at 3.55
and efficiency 3.44, which were averaged to 3.5 in the user category. They scored thesatisfaction of it at 3.22 and learnability 3.67, which were averaged to 3.44 in the task
category. Finally, they scored the memorability of it at 3.89, error 3, and cognitive load
3.78, which were averaged to 3.56. The overall score of the quantitative survey fromexpert review panel was 3.51, which is quite positive (> 2.5) on the 4-point Likert scale.

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Fig. 4. Demonstration video of the prototype of a hands-free AI
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University students. University students evaluated the effectiveness of the prototype at 3.29 and efficiency 2.88, which were averaged to 3.09 in the user category. Theyscored the satisfaction of it at 3.18 and learnability 3.65, which were averaged to 3.41
in the task category. Finally, they scored the memorability of it at 3.59, error 2.76 andcognitive load 3.44, which were averaged to 3.26. The overall score of the quantitative survey from university students was 3.25, which is positive (> 2.5) on the 4-point
Likert scale.
Suggestions for the prototype revision. In the interview, experts pointed out that theSR performance should be improved. Also, they suggested that more student questions
should be recognizable to the speaker and further proposed that the scenario should be
more elaborated for each function. Practically, they ideated that a brief introduction
to what the speaker can provide to students is needed. Students similarly proposed
processing more questions in the system, and the scenario should be more elaborated.
Consequently, the researchers were able to revise the AI speaker system before theexternal validation process, as follows: (1) The system awakening was simplified;(2) The number of error handling was increased for function 1; (2) Another entityintent-action was added to function 2 – providing additional tips not presented in theexperiment manual (Figure 3); (3) Another entity was added to function 3 to handle
questions such as “where should I dispose of a solution after titrating I2?”; (4) Moresentences were put to train the NLU model more precisely; and (5) The manual forusing the AI speaker system was prepared.
4.3

External validation

Figure 5 shows the implemented AI speakers in the ACE course and the manuals
provided to students.

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Fig. 5. AI speaker system installed in the science laboratory classroom (red circle) (a)and an example of the manual provided beside the speaker (b)Function 1 – solution-making helper. At the beginning phase of the experiment,
students asked the AI speaker system the amount of reagent needed to make a solution (Figure 6). First, a student called the system (Figure 6a) and requested a solution-making helper (Figure 6b). As the speaker did not catch what he had said, he bent
toward the speaker to let it hear his voice clearer, which succeeded in calling function 1(Figure 6c). Then, the student and AI speaker made a conversation about the reagent,concentration, and volume of the solution to make a potassium permanganate solution,and consequently, the AI speaker gave the student answer to the question (Figure 6d–f).
As the AI speaker was installed at the fume hood where the reagent needed to make asolution was kept, the student could make a solution according to the direction of the
AI speaker.

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Fig. 6. The use of AI speaker function 1 – solution-making helper

Function 2 – experimental process helper. At the middle phase of the experiment,
students asked the AI speaker system what they should notice in the procedure. At the‘titration in the non-aqueous solvent’ experiment session (Table 2), a student called thesystem (Figure 7a) before asking the speaker about the indicator’s color change at thetitration endpoint. However, at first, the AI speaker system did not detect student’s saying (Figure 7b). Although the student tried again, the speaker malfunctioned and played
pop music, which made the other students laugh (Figure 7c and d). It was the last time
she succeeded in calling the system and got the information – the indicator changes its
color from purple to blue (Figure 7e and f).

Fig. 7. The use of AI speaker function 2 – experimental process helper

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Function 3 – liquid waste disposal helper. At the end phase of the experiment,
students asked the AI speaker system in which container (acid, base, organic, or inorganic) to dispose of certain liquid waste. In Figure 8a, a student asked a colleague whereto dispose of Arsenic trioxide (As2O3), and in response, she suggested that they would
ask it to the AI speaker system (Figure 8a). She called the system, and it responded byasking her what waste liquid she wanted to dispose of (Figure 8b). When she answered
the AI speaker concerning Arsenic trioxide, the speaker gave her appropriate information – i.e., it is an inorganic waste liquid. Then she wondered, making an exclamation(“Oh~”) (Figure 8d).

Fig. 8. The use of function 3 – liquid waste disposal helper

System Usability Scale (SUS). The overall SUS score scaled to 0–100 was 63.85(Table 4). Bangor et al. [35] reported that the mean SUS score from about 3,500 surveys from 273 studies was 69.5. Therefore, the SUS score in this study is lower than
the average. Consequently, this score lies in the range of “good” SUS score with “marginal” acceptability [35]. Also, items 1 and 10 satisfied benchmark scores for average
SUS studies (≥ 3.39, ≤ 2.09, respectively), and item 7 satisfied that for industrial sense(≥ 4.19) [40]. These items show the strengths of the system developed in this study.
Table 4. The SUS quantitative survey result of external validation (N = 13) (1–5 scale)
Item No.

Question1

I think that I would like to use this product frequently.

2

I found the product unnecessarily complex.n

Mean [SD]3.69 (.85)*

p2.54 (1.05)

3p

I thought the product was easy to use.

3.62 (.77)

4I think that I would need the support of a technical person to be able to use
this product.n

2.77 (1.17)

5

I found the various functions in the product were well integrated.p

3.46 (.78)(Continued)

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Table 4. The SUS quantitative survey result of external
validation (N = 13) (1–5 scale) (Continued)
Item No.

Question

Mean [SD]6

I thought there was too much inconsistency in this product.n

2.62 (1.04)

7

I imagine that most people would learn to use this product very quickly.p

4.31 (.75)**

8

I found the product very awkward to use.n

2.54 (1.05)

9

I felt very confident using the product.p

10

I needed to learn a lot of things before I could get going with this product.

Overall SUS score

3 (.82)n

2.08 (1.12)*
63.85

Notes: p: positive statements, n: negative statements; *: satisfies an average item benchmark; **: satisfies a
common industrial item benchmark

In an open-ended question aligned with the SUS, students responded that the AI
speaker system was “easy to use,” “easy to learn,” “convenient,” “simple,” “intuitive,”or “not complex nor difficult,” which elaborates their response to the item no. 3, 7, andparticularly 10. Therefore, it seems that the perceived ease of usage led to students’
willingness to use the AI speaker system (item 1).
Meanwhile, students responded that there are some weaknesses in the system, which
are related. (1) The inflexible structure (plot) of the “command” or conversation “aswritten in the manual” and the number of questions they should articulate was inconvenient (seven students). (2) The SR accuracy of the AI speaker was problematic, which
led to the incorrect reaction of the system (e.g., not responding, playing pop music).
Perceptions of an AI Speaker System in a Science Laboratory Classroom(PASS-SLC). The overall average score of PASS-SLC items was 3.35 (Table 5).
Students responded that they could ask the speaker how they usually speak (M = 3.46) –however, the speaker did not understand their intentions well (M = 2.46). Notably, theanswers from the AI speaker was easy to understand (M = 4.31), with correct information (M = 3.85), consistently (M = 4.15), and reliably M = (4.08). However, the system was not convenient compared to calculators or smartphones (M = 2.69). Although
they responded that the laboratory class became safer due to the AI speaker system(M = 3.62), it was not comfortable to use the system compared to asking friends or
TA (M = 2.62). They perceived that using the system did not help them gain morescientific knowledge (M = 2.23). However, the same question for the scientific skills
scored higher (M = 2.85), and the question for the attitudes toward science class got a
positive response (M = 3.46). It seems that it was not due to the change in the laboratoryclass process (M = 2.77), the roles of instructors’ (M = 2.92) or students’ (M = 3), nor
the reduced performance time (M = 2.77). Rather, their fun (M = 4.46) and enjoyable(M = 4.54) user experience with the AI speaker system probably led to their increased
perception of attitude toward science class.

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Table 5. The PASS-SLC quantitative survey result of external validation (N = 13) (1–5 scale­)
Item No.

Question

Mean [SD]1

I was able to ask the speaker in the way I usually speak.

3.46 (0.88)

2

The speaker understood the intention of my question well.

2.46 (0.66)

3

The answer of the speaker was easy to understand.

4.31 (0.63)

4

The speaker told the correct information needed for the laboratory class.

3.85 (0.99)

5The information the speaker told was consistent.

4.15 (0.99)

6The information the speaker told was reliable.

4.08 (0.95)

7The system was more convenient for getting the information I wanted thanusing calculators or smartphones.

2.69 (1.03)

8

The laboratory class became safer than before through this system.

3.62 (0.87)

9

Using the system was more comfortable than asking friends or TA.

2.62 (1.26)

10

As a result of using the system, I could gain more scientific knowledge.

2.23 (0.73)

11

As a result of using the system, I could gain more scientific skills.

2.85 (1.14)

12

As a result of using the system, I could gain more attitudes toward scienceclass.

3.46 (1.2)

13

The process of laboratory classes using the system became different frombefore.

2.77 (1.24)

14

The role of the instructor has changed in the laboratory class using thesystem.

2.92 (1.04)

15

The role of the students has changed in the laboratory class using the system.

3 (0.91)

16

The laboratory performance time has been reduced when using the system.

2.77 (1.09)

17

The user experience of the system was fun.

4.46 (0.66)

18

The user experience of the system was enjoyable.

4.54 (0.52)

Overall average of PASS-SLC items

3.35 (1.17)

In an open-ended question aligned with the PASS-SLC, students responded thatrather than asking the system, “asking TA” or “using a calculator” to get information
would be more “convenient,” “fast,” and “correct” (items 7 and 9). Also, four students
problematized the structured way of asking the speaker as “complex” and “took time”(item 16). The reason for this was the SR accuracy (four students). And three responded
that the AI speaker system would be useful when the SR accuracy increases.
Pros, cons, and suggestions for revision. Students pointed out the pros of the AI
speaker system implemented in the classroom as follows: First, five out of thirteen
acknowledged the high “accessibility” of the system, which could be used “easily”“whenever” they wanted, and this feature drew students’ interest (two students)(items 1, 12, 17, and 18 in the PASS-SLC; Table 5). Second, three students appreciatedthat the system had freed their hands, lest they repeatedly put on and off the gloves
to touch things other than experimental, which supports keeping safety (one student).
Further, three students responded that the workload of the TA has been reduced due tothe AI speaker system.

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5

Discussion

5.1

Research question 1

The ADDIE process of developing and validating the hands-free AI speakers supporting hands-on science laboratory classes shown throughout this study answers
research question 1.
It should be noted that the available technology largely shaped the characteristics ofthe prototype. First, the AI speaker system developed in this study is ‘explainable.’ Asexplainability is becoming increasingly important for ethics in AIEd [7–8], the characteristic of AISEd should be appreciated and further pursued. Meanwhile, one of thereasons for non-satisfying SR accuracy is attributed to the current state of the Korean
NLP (cf. [41]), which is lower than that of the English. Thus, we can anticipate different
responses if the equivalent research was conducted in English-speaking situations or
any other context, which calls for further study [5].
5.2

Research question 2

The responses of experts and university students had commonalities and differencesin the PACMAD items. The categories that scored highest and lowest were similar. For
example, the learnability, memorability, and cognitive load categories took the first to
third ranks in both groups (Table 4). The issue of explainability mentioned above canbe connected to the learnability and memorability in the PACMAD. Also, reducing the
cognitive load is the main contribution of AIEd [7–8]. These signify that the AI speakersystem satisfied those criteria, possibly leading to students’ concentration on hands-on
experimentation. Further, the experts and students said the error is the most concern in
the PACMAD in parallel, providing corresponding suggestions for revising the prototype. These showed the possibilities and current status of the AI speaker system in thescience laboratory.
However, there were some differences between the perceptions of experts and students. The experts’ PACMAD scores were higher in every category and thus overall
items compared to those of students. Particularly, the efficiency showed the most gap
between experts (3.44) and students (2.88) (Table 4), which was backed up by the
open-ended questions. For example, experts said, “an AI speaker is efficient because it
answers to the query immediately,” “hands must be used in the laboratory environment
in general, but one can get an answer for his/her question by just an articulation,” “if it
can be realized, it would be definitely helpful for the safe experimentation and would
reduce time,” and “it adds to efficiency because one can ask for information and get ananswer.” On the contrary, students said that “it takes a too long time to get an answer,”“one may feel jammed up because it takes a long time asking and getting an answer,”and “I think it is very good and positive, but it seems to have a long way to go.”Reflecting on the differences, it seems that the experts’ background experiences
made them evaluate the prototype positively. Meanwhile, students may have imagined the realistic situation the AI speaker is implementing in their laboratory classroom, making them not give high scores. Future research that scrutinizes why there

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are disparities in the perceptions of introducing AI (speaker) into a classroom between
educational experts and students would be meaningful.
5.3

Research question 3

First, the mobile characteristics of the AI speaker enabled its optimal use according toeach function – from the fume hood, laboratory table, or near the liquid waste container.
Second, it is manifest that the unsafe situations in that students touch non-experimental
things with their gloved hands reduced significantly. It inherently leads to the third
strength, the incorporation of another learning mode – i.e., auditory one – into the science laboratory class to support students’ experimentation. Much literature has shownthat visual (V), kinesthetic (K), and reading/writing (R) learning modes are importantin science laboratories in the hands-on inquiry, minds-on inquiry, and lab report writing [12–15]. However, almost no research seems to have pointed out that the auditory(A) learning mode can be utilized in a science laboratory class, which complements
the VARK multi-modal learning [42]. This implies that the implementation of the AI
speaker in the lab raised a significant, noteworthy theoretical thesis for the scienceeducation field.
The interpretation of the SUS score needs caution. According to Bangor et al. [43],while the mean SUS score of interactive voice response (IVR) systems was 73.84(N = 401), that of a combinatory Web/IVR system was 59.45 (N = 50). Further, recent
studies on the usability of Amazon Alexa showed a score of 63.69 (N = 61) [39]. These
indicate that the SUS score of this study (63.85) is intermediate as an AI speaker. Therefore, according to the SUS score, the product of this study shows promises and suggests
future research implications of AI speakers supporting hands-on science laboratories,
although the immediate industrial or practical implications might be few.
The result of the PASS-SLC survey shows that the most strength of the AI speakersupporting the science laboratory class can be its affordance to draw students’ interestin a science laboratory class. In the PASS-SLC, students responded that they did not
directly gain more knowledge or skills through the system – however, if we expect theaffective domain to lead to active engagement in laboratory activities, the developmentof knowledge and skills might follow in a long-term sense.
Manifestly, the most urgent problem for the suggestions for future revision is improving the AI speaker’s SR accuracy (in Korean) to catch students’ natural language articulations. It would enable more functions and a flexible conversation structure of AISEd
optimized in various situations, reducing resources spent on error handling.
However, the instructional designers shall not just rely on the advancement oftechnologies but contemplate the possible remedies for problems listening from thelearners. University students, who were pre-service chemistry teachers, suggested bothincreasing SR accuracy and allowing flexible conversations. At first glance, it seems
contradictory and has a trade-off relationship. However, students suggested the keyword-based conversation for the AI speaker system in the external validation. It seems
possible to catch two birds with one stone, even with the current technologies. Research
focusing on the type of conversation (natural language-based versus keyword-based)in AISEd should follow.

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6

Conclusion

Using the RP method, we designed and developed a hands-free AI speaker-based
system that answers student queries concerning solution-making, experimental processes, and waste liquid disposal, corresponding to the initial, middle, and final phasesof a hands-on laboratory class. The system was internally validated by usability testsof 9 expert panels and 18 university students and then revised. The revised system
was externally validated in an analytical chemistry experiment class for 3 sessions
with 13 university students. We presented the result of the prototype development andinternal and external validations with quantitative and qualitative data. The AI speakersystem enabled students to use the auditory learning mode in the laboratory while concentrating on the experimentation with their hands in the external validation. It can beconsidered as opening a channel for the auditory learning mode in science laboratoryclass to the previous visual, reading/writing, and kinesthetic modes to complement the
VARK multi-modal learning.
One of the novelties of this case study lies in its research field – a science laboratoryclassroom filled with Korean-speaking learners. Ironically this leads to the need formore generalizable instructional knowledge (see [18]). Although this study carefully
designed, developed, and implemented an AI speaker system for the science laboratoryclass, it does not provide comprehensive principles, guidelines, or models for future
designers of AI speakers used in a classroom. Therefore, research that presents designprinciples for or model of AISEd in authentic teaching and learning sites should follow.

7

Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF)
grant funded by the Korea government (MSIT) (No. 2021R1F1A1058192).

8

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9

Authors

Gyeong-Geon Lee is a Ph.D. candidate in the Department of Chemistry Education,
College of Education, Seoul National University. He holds a B.S. (Chemistry Education) and a B.E. (Computer Science & Engineering) from Seoul National University.
His research interests pertain to various fields of educational studies, from history andphilosophy of science education, curriculum studies concerning competencies, science teaching and learning focusing on inquiry and laboratory, educational technology
focusing on instructional design, and artificial intelligence in education for automatedassessment and classroom environment. He seeks to incorporate various approaches
to science education into a comprehensive knowledge base (crusaderlee@snu.ac.kr).
Minji Choi is a master’s student in the Department of Chemistry Education, Collegeof Education, Seoul National University. She holds a B.S. (Chemical Engineering)from University of Seoul. Her research interests pertain to non-face-to-face experimental classes, game-based learning, and school science. She studies how students canhave positive experiences with science classes and improve science academic emotion(minji0102@snu.ac.kr).
Taesoo An is a master’s student in the Department of Chemistry Education, Collegeof Education, Seoul National University. He has educational experience in developing countries, including Korea. He is interested in the education that all students areinterested in, and the core competencies that must be learned in the future. His field of
research is interested in the universal application and expansion of scientific inquiry
methods beyond the scientific field (obrigado@snu.ac.kr).
Seonyeong Mun is a master’s student in the Department of Chemistry Education,
College of Education, Seoul National University. She holds a B.S. (Chemistry Education) from Seoul National University. Her research interests pertain mainly to history
and philosophy of science and science education, science teaching and learning focusing on inquiry and laboratory. She seeks to how students can participate in scienceclasses more meaningfully, construct and acquire science knowledge in the context ofscience (suyo2478@snu.ac.kr).
Hun-Gi Hong is a Professor in the Department of Chemistry Education at theCollege of Education, Seoul National University, Seoul, Republic of Korea. He finishedhis PhD in Analytical Chemistry from the University of Texas at Austin and postdoctoral fellowship at the Beckman Institute of Science and Technology, University of
Illinois at Urbana-Champaign. He has been a Chemistry Professor at Sejong University
and a visiting professor at Iowa State University. His research interests include various
fields of analytical electrochemistry, chemistry education, gifted student education, andeducational technology (hghong@snu.ac.kr).
Article submitted 2022-08-22. Resubmitted 2022-10-29. Final acceptance 2022-10-29. Final version
published as submitted by the authors.

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Paper—Enhancement the Educational Technology by Using 5G Networks

Enhancement the Educational Technology
by Using 5G Networks
https://doi.org/10.3991/ijet.v18i01.36001

Duha Khalid Abdul-Rahman Al-Malah1, Ban Hassan Majeed2,
Haider TH. Salim ALRikabi3()Northern Technical University, Mosul, Iraq
College of Education for Pure Science/Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq
3
Electrical Engineering Department, College of Engineering, Wasit University, Wasit, Iraq
hdhiyab@uowasit.edu.iq
1

2

Abstract—With the spread of global markets for modern technical educationand the diversity of programs for the requirements of the local and global market
for information and communication technology, the universities began to race
among themselves to earn their academic reputation. In addition, they want toenhance their technological development by developing IMT systems with integrated technology as the security and fastest response with the speed of providing
the required service and sure information and linking it The network and usingsocial networking programs with wireless networks which in turn is a driver ofthe emerging economies of technical education. All of these facilities opened theway to expand the number of students and solve the problem of accumulation,collection and analysis of data by storing it with large, expanded and automatically interconnected databases between university places and departments to provide services adapted to the desire of demand. This research dealt with a sample
from of the academic’s opinions and students. The sample is 319 questionnaires.
It concluded that each of the infrastructure, devices, Internet of things, smart
classrooms, and administrative database, with the presence of the fifth-generation
network and its equipment, have a statistically significant correlation with technical education technology.
Keywords—educational material, educational strategies, systems andapplications, produce, educational devices

1

Introduction

AI and 5G networks have been identified as critical areas of innovation and creativity
necessary to enable smarter and more proactive technology communities. The next
generation is the fifth generation technology, the generation of virtual technology withmobile communication standards equipped with applications and services of the Internet at its new speed, which showed a significant improvement in the performance oftechnical education technology, which enhanced confidence between the user and thetechnological investor by sensing the magnitude of data, managing and coordinating

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network resources, and providing connected and independent smart systems [1].
Therefore, higher and technical education institutions have embarked on the processof shifting to automated education, including an automated electronic teacher andvirtual reality, including virtual classrooms, smart boards, 3D artificial intelligencelaboratories, and high-resolution photographic sensor cameras. Coding and numbering
university students and benefiting from the advantages of the Internet of Things andremote sensing by supporting them with the fifth generation networks FG-ML5G, modern computers and mobile devices, and a team specialized in studying cases, services,
requirements, interfaces, protocols, algorithms, and network architecture for machinelearning and data formats [2]. Education or teaching is the intentional organized designof the experience that helps the learner to achieve the desired change in performance,
targeted learning outcomes. Learning is a subjective activity carried out by the learner,with or without the supervision of the faculty, with the aim of acquiring knowledge
or skill or changing behavior. Learning is everything that a person acquires throughpractice and experience, and it is the other side of the education process and its product,and it is associated with it so that one cannot be separated from the other. When talking
about education, it is necessary to shed light on learning to form a clear and complete
picture on the subject. Teaching differs from learning in that teaching is an activity
undertaken by a qualified person; To facilitate the learner’s acquisition of the required
knowledge and skills, while learning is the self-efforts made by the learner to acquire
the knowledge and skills he seeks to acquire [3]. Teaching strategy is the strategies usedby a faculty member to improve student learning. It can be defined as a set of generalrules and outlines that concern the means to achieve the desired goals of teaching.
It refers to the methods and plans followed by the faculty member to reach the learning
objectives. It is the set of activities or mechanisms used (presentation – coordination –training – discussion) in order to achieve specific teaching objectives. Thus, it includes
two components, namely, the method and the procedure, which together form a total
plan for teaching a lesson, unit, course, or other. That is, a faculty member may go
according to his own style of teaching, approaching any teaching method. He chooses
it, but it does not deviate from a general framework that has its general teachingprocedures. It includes teaching objectives and the movements that a faculty member
makes and organizes; to walk according to her in his teaching. Organizing the classroom environment and managing the classroom. Learners’ responses to stimuli presented, planned, and organized by the faculty member [4, 5]. Learning strategies are
the behaviors and actions a learner engages in that aim to influence how you process
information and learn different tasks. It is also defined as the behavioral patterns andthinking processes that learners use and affect what has been learned and the treatmentof learning problems. Learning is strategic when learners are aware of the specific skills
and strategies (specific procedures and methods) that they use in learning, and control
their attempts to use them [6–8].The current era is characterized by expansion in all
different fields and to ensure keeping pace with this expansion Knowledge, scientificdevelopment and technical employment, the role of education becomes the development of the learner in the aspect Cognitive and skillful, through multiple teaching methods and methods, instilling in the learner the direction of employment technology ineveryday life. Accordingly, teaching aids represent a set of devices, tools, and materials
that a faculty member uses to improve the teaching and learning process. Among the138

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devices that helped in this computer, which contributed in many ways to teach, including educational games, and a problem-solving environment [9, 10]. Instructional “educational” material can refer to the rate of development or progression of a student’s
cognitive development, abilities, interests, and other aspects. It allows the student todevelop strategies for assessing, planning, and organizing his or her own learning. The
characteristics of the educational material vary according to the objectives, characteristics of the students and conditions of study, as well as infrastructures and access totechnologies. You can design educational material for different contexts in such a waythat your design sparks interest and curiosity about the issue. So that it is a source of
information and directed to motivate. It can be adapted for use with or without teacher
assistance. It can be used individually or in combination. That is, it is multi-use [11, 12].
1.1

Educational devices, systems and applications

The use of smart devices and harnessing their capabilities in the service of teaching
and learning is one of the necessities of this age, and diversity in teaching methods is
one of the priorities of the teacher of this century. It is also necessary to address challenges and facilitate ways and means, so there must be material and human support,
training and management to introduce this technology and work on it in the educational
field [13, 14]. Due to the widespread use of smart technologies, most universities havebeen keen to invest in supporting the educational process from various aspects by using
the options provided by these technologies such as SMS, mobile learning management
systems and applications to attract learners to integrate into the electronic environment
they provide. Most of today’s learners who are involved in university education havebecome familiar with the use of mobile devices of all kinds, especially mobile phones
and tablets. As most students are younger than the tablets or stationary computers theyuse in their daily lives, that is, they have grown up with computer technologies thathave enhanced and enhanced many aspects of their lives, including: Education [15].
So Educational technology is based on a theoretical basis of principles, ideas andtheories based on it, and it falls within an applied scientific field in which ideas and theories are put into practice and scientific practice. It is based on the entrance of systems,
meaning that it is an integrated system that includes man, machine, ideas, practicalmethods, and management [16, 17].

Educational Technology

Instructional Technology

Instructional Aids

Fig. 1. The difference between educational technology and instructional technology

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1.2

Educational technology

It is difficult to ignore the growing role of educational technologies in the integrated
e-learning adopted by most universities, as these technologies have contributed totransferring teaching and learning to a new level in which learning resources are easily accessible when needed. It also provided an educational environment in which theteaching and learning processes are conducted by the teacher and the learner, throughmobile devices and wireless networks wherever they are and at any time they want to.
This environment is characterized by its wide spread as it is not restricted by placeor time and gives a special character to teaching and learning activities and emphasizes self-directed learning in which educational applications play an important role inachieving it [18, 19]. Technological progress has led to the emergence of new methods
and methods for indirect education, which depend on the employment of technological innovations to achieve the required learning, including the use of computers andits innovations, satellites and satellite channels, and the international information network, in order to provide learning throughout the day and night for whoever wants it
and in the place that suits him [20], Through various methods and methods supported
by multimedia technology with its various components, to provide educational content
through a combination of written and spoken language, static and animated visual elements, and various audio-visual effects and backgrounds, which are presented to thelearner through the computer, which makes learning interesting and enjoyable, andachieved with the highest efficiency, With the least effort, and in the least time, whichleads to the quality of education [21, 22]. Educational technology has also witnessedwide changes due to the launch of social media, information storage and transmission,
the use of visual and audio media, the production of its educational outcomes and it’ssharing among students. The role of technology has expanded from being a mere tool
for study and inquiry to an integrated approach and use in technical education. Technical education specialists are responsible for educating and guiding their students in the
optimal and beneficial use of their studies [23, 24]. In the sixties of the last century, theexploration of the possibilities of self-service based on technology began to complement or replace the personal services provided to the student category and performedby the staff of the Student Affairs Division at a time when services were considered lessimportant in creating economic value and financial returns from student registration,
and with the growth of technology and consumer desire Because of the technology
interfaces and their available capabilities to facilitate the student’s task and complete histreatment as quickly as possible, educational institutions have tended to move forwardwith the advanced technological reality [25, 26]. Educational technology as any activity
or benefit based on technologies provided by service providers so that customers canperform the service or parts of the service themselves, in the field of educational technology applications, the service provider is a machine that the customer uses to performthe service himself, which facilitates The process of replacing student affairs personnel
who were necessary in the provision of the traditional service [27, 28]. Other similarauthors have suggested terms such as self-service technologies, which are defined astechnological interfaces that enable customers to produce a service independent of the140

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involvement of direct service personnel [29]. Research has recognized the benefits ofeducation technology to clients including time and cost savings, greater control overservice delivery, reduced waiting time, higher level of personalization, convenience oflocation, and enjoyment of using technology, efficiency, flexibility, greater satisfaction
and mental innovation .There are quick ways to transfer information (Super High Way
Information) from one place to another, and the emergence of the international information network (World Wide Web) known as the Internet, and its employment in all
walks of life, shows the importance of information As a commodity that is bought andsold [30, 31].
1.3

ET tools

1. Educational material: the professor, the educational content, the student [32, 33].
2. Educational strategies: interactive education, educational content diversification,
educational investment, simulation, cyber security, reality virtual, program integrated [34].
3. Systems and applications: artificial intelligence programs such as the automatic
teacher system, the human senses simulation system and addressing its educational problems, intelligent platforms, smart network protocols and algorithms, data
management base, fog computing, cloud computing, internet of things, machinelearning [35].
4. Produce: economic investment, social investment, educational investment [36].
5. Ed. devices: laptop computer (tablets), course projector, digital camera, smart boards,
digital interactive whiteboards, communication network, digital projectors, intelligent career path driver, high frequency equipment, sub band wireless coverage, fiber
optic cables, millimeter wave radio links and relays, robotics, real time system, distributed real time system, network security [37, 38]; as noted in Figure 2.

Fig. 2. Educational technology tools

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2

Literature review

The research dealt with a group of previous research that dealt with educational
technology and fifth generation networks. It was presented in table to show the similarities and the most important aspects that it touched upon.
Table 1. Presentation of the most important research papers that dealt with educational
technology and fifth generation networks
Authors

Objectives

Conclusions[39]

It focuses on interactive learning duringlectures while discussing and evaluating
various applications of educational
technology in the field of medical
education. Medical schools regularly use
lectures and other group learning sessions.
It is well recognized that encouraging
interactive learning in big groups can bedifficult. The development of technology to
improve engagement and communication isa promising addition to interactive lectures.

Education in the medical field now includes
a variety of educational technologies. To
maximize learning results, technology
use and integration should be guided
by educational needs. Prerequisites forthe best success include a specific job
and objectives. Selected technology canpromote interactive learning in educational
activities. To increase students’ involvement
and participation in lectures and othergroup learning sessions, many technology
applications have been deployed. To find ordevelop the proper technological tools for
effective instruction based on educational
theories, collaborative and ongoing efforts
are needed.

[40]

This paper dealt with the possibilities
offered by the fields of artificial intelligenceand their use, and the process of evaluating
exam results.

Many faculty members have struggled
to use this technology because of theirextensive knowledge and the students’limited understanding of it.

The current study aimed at the extent of the impact of the fifth generation networks
with their mobile devices, and the 5G network as a technology that brings by its natureaspects such as high speed, extremely low latency, high bandwidth and wireless network communications that contributed to the establishment of the Internet of Things
as an important part in technical education, facilitated a major paradigm shift The educational process, in which users interact with big data, and what huge data warehouses
provide, has reinforced the goal of educational investment, entering the labor market,
and offering educational products through social media and websites, as well as dealing
with artificial intelligence applications to enter the technical field as robots and smart
language programs.

3

Methodology

To describe the study sample, a random sample was selected and the opinions of theacademic staff and students were taken. The site of the current study is the NorthernTechnical University. The electronic questionnaires were collected, which numbered
319 valid questionnaires for analysis. The confirmatory factor analysis was chosen that142

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deals with issues of validity and reliability in measurement by examining the validity
and reliability of the scores. On the variables used within 9 criteria set to measure theextent to which the hypothetical model matches the analytical model. Given an acceptable level of validity and reliability of the result, scores are used in statistical analysis tomodel structural equations, correlated factors, and observed variables that measure eachfactor. To explore the number of factors, present, whether the factors are interrelated,
and the observed variables that seem to measure each factor better. For the study variables and using the Amos v26 analysis system, the questionnaire contained 17 questions that were adopted as variables and divided into independent dimensions (x1, x2,
x3, x4, x6, x7, x8, x9, x10, x11, x12) variables represented the educational technology
variable and its axes of internet of things, database management, artificial intelligence,
smart education system, while the variable is the fifth generation, the dimensions of thedependent were calculated (y1, y2, y3, y4, y5), as shown in Figures 3 and 4.

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Fig. 4. Confirmatory factor analysis (standardized estimate)
Source: Prepared by researchers based on the results of the analysis (Amos V26).

4

Results and discussion

Table 2’s computation of degrees of freedom demonstrates how Amos determines
degrees of freedom as the difference between the number of distinct sample moments andthe number of distinct parameters requiring estimation. Variances and covariances arealways included in the count of distinct sample moments. When you estimate means andintercepts, it also includes sample means. Multiple parameters that are forced to be equal
to one another count as a single parameter when calculating the total number of different
parameters to be estimated. Fixed parameters with unchanging values have no effect.
Table 2. Degrees of freedom computation (default model)
Number of distinct sample moments:

153

Number of distinct parameters to be estimated:

39

Degrees of freedom (153–39): DF

114

minimum chi-square CMIN:

316

When calculating the probability ratio/(degrees of freedom) CMIN/DF, a value of
2.8 appeared, which is a value between 2 and 5, which means that the value of the144

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chi-square did not exceed the upper bound and as calculated by equation No. 1, theminimum chi-square is divided by the degrees of freedom.
CMIN/DF = 316/114 = 2.8...

(1)Chi-square, which is the ratio between the value of the number of sample moments,
which must be from 2 in the case of an exact match, and less than 5 in the case of
acceptance of the model.
We note in Tables 3 and 4 the imposed model is identical to the data, which iscompared with the saturated model at 0 degree of freedom, which has no value to bedone through only theoretical calculations. In Table 3, we see the minimum varianceto be chi-square estimation is a method of estimation of unobserved quantities basedon observed data. In certain chi-square tests, one rejects about a population distribution
because a specified test statistic is too large. One effect of its application is that the test
statistic does indeed roughly follow a chi-square distribution. The number of degrees
of freedom for each parameter evaluated using this method is typically reduced by 1.
Table 3. RMR, GFI
Model

RMR

GFI

AGFI

PGFI

.066

.895

.859

.667

Saturated model

.000

1.000

Independence model

.333

.414

.341

.368

Default model

Table 4 shows the root mean squared (RMR), is a statistical measure of the valuesof varying quantities. It is especially useful when the values vary into positive and negative, Goodness-of-fit (GFI) establishes the discrepancy between the observed values
and those expected of the model in a normal distribution case, The adjusted goodness of
fit index (AGFI) corrects the GFI, which is affected by the number of indicators of eachlatent variable, The modifications are made to penalize less parsimonious models andfavor simpler theoretical processes over more intricate ones. The fit index decreases asmodel complexity increases. PGFI is a parsimonious fit index (based on the GFI).
Table 4. Baseline Comparisons
Model

NFI
Delta1

Default model.856

Saturated model

1.000

Independence model

.000

RFI
rho1

PCFI
.756

.829

.000
.000

The normed fit index (NFI) examines the difference between the null model’s chisquared value and that of the hypothesized model. Relative Fit Indices, also called theincremental fit, includes a factor that represents deviations from a null model; so theseare sometimes called comparative indices.
Table 5 showed that the data were collected in a random way, and that the sample size is large, reaching 319, and the presence of (connectivity) in the sense of theiJET ‒ Vol. 18, No. 01, 2023

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dependent variable as a continuous variable, with moderation between the approved
variables and random error, no abnormal values appeared in the statistical analysis, aproblem appeared. There is a multiplicity of linear relationship between the independent variables, so we relied on the unweighted least squares method for the free scale.
Table 5. The most important indicators of good conformity and the limits of its acceptanceAcceptance Limits

Pointer

It was 2.8 which is less than (5) accepts the assumed model

Likelihood ratio (degrees of freedom)
CMIN/DF
Goodness of Fit Index GFI

the value is 0.895, which is greater than or more (0.90),
which means a good matchIts value is 0.859 which means production quality matching

Adjusted Goodness of Fit Index AGFIIts value is 0.856, which indicates the good quality of themodelNormative Fit Index NFIIts value is 0.756, which indicates the good quality of themodel

Parsimony Goodness of fit index PGFIIts value of 0.829 indicates the good quality of the modeland its conformity with the data

Relative Fit Index RFI

Its value is 0.066 This indicator indicates a good fit of themodel

Root Mean Square Residual RMR

Diagram (1) shows the linear regression equation for the independent study variable
fifth generation networks and the study variable approved by the technical education
technology and the matching ratio for the hypothetical model is R2 = 0.998, and thelinear equation No. (2) represents the effect between the variable X and the Y variable.
Y = 3.31E-4 + 0.4*x …

(2)

Fig. 5. It shows the regression equation between the variables146

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5

Conclusions
The conclusions represent an analytical outcome and are summarized as follows:

1. All opinions were directed by all academics to gain the experience and competencenecessary to create interactive lectures that enhance participation between professor
and student, which improved the outcomes of technical education.
2. The methodological content contributed to changing the strategies of teaching methods and the reliability of providing correct and accurate information to the student.
3. Adopting smart teaching systems that enhance the creativity and innovations of boththe professor and the student.
4. The availability of high-frequency equipment accelerated the delivery of information and in a short period of time, reduced the wages of studies, and did not rely
on private lessons for the spread of lessons and lectures through easy-to-use study
channels and platforms.
5. The educational technology industry using wireless coverage with sub-bands
with offers and incentives for commercial applications between public and private
universities, encouraging scientific competition and increasing scientific research
activity.
6. Opening new markets for technical education technology, which contributed toincreasing the professor’s degree and scientific activity, which reflected positively
on raising his financial level.
7. Encouraging pilot projects for the purpose of testing them and then engaging themin the labor market.
8. The values of the correlation coefficient between each approved base variable of thefifth-generation networks and the approved variable of technical education technology at the level of the surveyed institution was identical with a ratio of R2 = 0.998,
which is an integrated and comprehensive percentage of the trend towards the use oftechnical education technology.
9. There are parameters whose results matched the results of the study, which made thedefault model identical to the standard model. The relative probability indicators,
the matching quality index, the standard congruence index, the economic conformity
quality index, the relative matching index, and the root mean index of the residualsquares were all identical.

6

Recommendations

1. The necessity of empowering workers regarding modern technologies and evoking
the technical culture in conjunction with the requirements of the technical transformation process without making huge mistakes that reflect a negative result that leads
to the rejection of the digital transformation process and the preservation of the old
traditional system.
2. Working to secure common opinions for the advancement of technical education
to higher levels and to come up with solutions and results that contribute to thestrengthening and continuation of the work of the technical education technology
system.

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3. Avoiding the idea of technical control in the field of work, but must secureways to ensure the success of its use and find ways to support it.
4. The necessity of choosing reliable infrastructures and devices that can berelied upon to transition to technical education technology for administrative matters that would expedite the tasks of the professor, student and administration, andpreserve data and information and not lose it.
5. Adopting modern thought and innovations and motivating innovators to reachthe goals of technical education technology and sustain their achievement.

7

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Authors

Duha Khalid Abdul-Rahman Al-Malah: Lecturer, is the Reporter Department ofTourism and Hotel Management, Responsible of the Central Library Division, Instructor for Computer Applications Course.

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Paper—Enhancement the Educational Technology by Using 5G Networks

Ban Hassan Majeed, she is an Assistant Professor and one of the faculty member in the Department of Computer Science, College of Education for Pure Sciences /Ibn Al-Haitham, University of Baghdad, Baghdad, Iraq. Her current research interests
include Mathematics Education, Educational Technology, Digital Education, Smart
Learning, Multiple Intelligence in Teaching and Learning, and Thinking with its skills.
She holds an international assessor certified Publons and Academy peer reviewer.
She is also an active researcher in the field of ICT in education. She is currently amember of editorial board in the peer review process for several international journals.
The number of articles in national databases – 7. The number of articles in international
databases – 20. E-mail: ban.h.m@ihcoedu.uobaghdad.edu.iq
Haider TH. Salim ALRikabi: He is presently Asst. Prof and one of the faculty
Col-lege of Engineering, Electrical Engineering Department, Wasit University in
Al Kut, Wasit, Iraq. He received his B.Sc. degree in Electrical Engineering in 2006
from the Al Mustansiriya University in Baghdad, Iraq. His M.Sc. degree in ElectricalEngineering focusing on Communications Systems from California state university/
Fullerton, the USA in 2014. His current research interests include Communications
systems with the mobile generation, Control systems, intelligent technologies, smart
cities, the Internet of Things (IoT), Renewable Energy. Al Kut city–Hay ALRabee,
Wasit, Iraq. E-mail: hdhiyab@uowasit.edu.iq. The number of articles in national databases – 30, and the number of articles in international database – 60.
Article submitted 2022-10-12. Resubmitted 2022011-24. Final acceptance 2022-11-29. Final version
published as submitted by the authors.

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Paper—Influences of Problem-Based Online Learning on the Learning Outcomes of Learners

Influences of Problem-Based Online Learningon the Learning Outcomes of Learners
https://doi.org/10.3991/ijet.v18i01.36705

Chi Zhang(*)

School of Economics and Management, Huanghuai University, Zhuamdian, China
20121261@huanghuai.edu.cn

Abstract—Influenced by the COVID-19 pandemic, online learning has
become a major learning mode for most university students in China. For highquality online teaching, teachers must optimize the teaching mode and strengthen
the enthusiasm of students in classroom learning. In this study, a comparative
experiment was carried out based on the course Cross-border E-commerce Customer Services for sophomore E-commerce majors at Huanghuai University inZhumadian City, Henan Province, China. In the experiment, the teaching effectof the problem-based learning (PBL) mode in higher education was verified andgender differences in learning outcomes of learners who accepted PBL were analyzed. Results showed a significant difference at the 0.01 level between pre- andpost-test results of the experimental group (t = −11.367, p = 0.000), proving theteaching effect of PBL. The academic performances of the experimental classare significantly better compared with those of the control class, indicating that
PBL is more beneficial to improve learning outcomes of students than traditional
teaching methods. Gender has significant influences on post-test results at the0.01 level (p = 0.002 < 0.01). The median of final exam scores of male students
is obviously higher than that of female students. Research conclusions can provide important references to test the effectiveness of PBL in learning outcomesof a specific subject, construct a PBL mode that can promote deep learning, andfacilitate development of university teaching from knowledge teaching to corequality training of students.
Keywords—online teaching, teaching mode, learning outcome, paired sample
T-test, independent sample T-test

1

Introduction

The updating and development speeds of knowledge are far higher than the acceptance speed of people. Internalizing and applying knowledge in a short period is often
difficult for learners. Given that life is surrounded by various types of fragmented andentertainment information, gaining a deep understanding of knowledge is even moredifficult for the student group. Most students only have a superficial understanding.
To develop good self-study habits through independent learning, university students
need to use diversified learning strategies positively under the effective guidance ofteachers, develop complete critical thinking and problem solving ability, train the152

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consciousness of cooperation and exchange, and emphasize individual enthusiasm
in reconstruction of new and old knowledge, thereby developing learning habits withhigh-order thinking ability and achieving deep understanding and effective migration.
However, the traditional simple exam-oriented education mode can no longer meet students’ needs for life and comprehensive quality improvement. The traditional teachingmodes have long been centered on teachers, so that they have been put in a passive
learning position. Teachers have single teaching methods instead of providing effective
teaching according to the learning needs of learners, and they pour teaching contents
into students repeatedly. How to make students develop and improve their independent
learning ability truly is key to current teaching reform in disciplines in universities. One
important university teaching goal and requirement is to further develop and improve
the independent learning ability and learning outcomes of students.
PBL is a mode that is centered on learners and guided by problems. Introducing
PBL into teaching activities in universities is critical for its many obvious advantages
compared with traditional teaching methods. PBL advocates that students focus onproblems during learning activities. Students can collect relevant information, review,screen, and discuss the collected information in a group, and finally develop solutions
to their problem. PBL is more conducive to developing the independent learning enthusiasm of students fully. PBL is centered on problems and students are asked to think
about the problem and discuss it with a group. Students may take the initiative to seek
solutions in this process, thus strengthening their learning interests and increasing theirlearning initiation. Through the scenarios of well-designed practical life problems, PBL
helps learners in ways and methods to find solutions during self-study from perspectives such as guess hypothesis, scheme design, experimental exploration, data analysis,summarization and exchange, and reflection. Moreover, the goal and philosophy of
PBL are highly consistent with deep learning. Therefore, a series of targeted measures
to improve teaching activities in universities and suggestions to improve independent
learning ability of university students were proposed based on comparative analysis of
the teaching effect of the PBL mode, enabling an increase in the overall teaching levelof universities.

2

Theoretical basis and hypotheses development

2.1

Theoretical basis

PBL theory was proposed by Hmelo-Silver, C. E [1], who believed that PBL mainly
includes teachers’ careful selection and design of problems, learners solving the problem through critical thinking, and developing independent learning ability and teamcooperation skills. PBL is viewed as a teaching method that can help learners to acquire
methods, thinking, and ability to solve problems in the learning process. PBL combines
the problem-based teaching of teachers and problem-based learning of students andviews problems as the source of learning. Students cooperate, explore, and complete
learning contents independently. A PBL mode that can improve students’ ability is different from traditional teaching. PBL is a teaching mode based on problems, centeredon students, and implemented by group cooperation. PBL highlights the dominant roleiJET ‒ Vol. 18, No. 01, 2023

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Paper—Influences of Problem-Based Online Learning on the Learning Outcomes of Learnersof students, while teachers are responsible for supervising and facilitating learning ofstudents as guiders and organizers of students’ learning activities, and they play animportant role in stimulating the enthusiasm of students in learning. PBL also emphasizes putting learning in complicated and meaningful real problem scenarios, in which
students find the learned contents and choose and use knowledge and methods of multiple aspects to solve the practical problem, thereby establishing the knowledge system
successfully. In the teaching process, PBL advocates group cooperation positively andmakes reasonable grouping according to students’ requirements for individual development. Given the ability and interests of students, PBL assures consistent inter-grouplevel and encourages team members to communicate and discuss mutually, exhibit andreport on the lesson, and finally make summaries and evaluations. PBL stimulates students to explore and innovate around problems continuously by setting the problem
scenarios, thereby causing obvious influences on deep learning of learners.
2.2

Hypotheses development

PBL plays a very important role in the international education and teaching field.
The primary effect of PBL is that it helps students in strengthening their thinking about
structural problems. Based on this thinking process, students solve existing problems
using divergent thinking and previously learned knowledge. Studies on how PBL influences the learning outcomes, motivation, and learning strategies of learners are introduced as follows. Qomariyah, S. N [2] carried out a comparative study on 80 students
from two classes by using a quasi-experiment. Results showed that PBL and traditionalmodes had some influences on learning outcomes, but the influences of the former
were significant. Belland, B. R et al. [3] analyzed results of PBL in 33 empirical studies
and found that whether PBL influenced learning outcomes was related with sample
size and experimental method to a certain extent. Distlehorst, L. H et al. [4] carriedout comparative experiments of students from nine graduating classes in the School of
Medicine, Southern Illinois University, and found that PBL teaching helped students
to obtain better clinical performances and caused significant differences in medical
knowledge accumulation, clinical reasoning, and non-cognitive behaviors. Preeti, B
et al. [5] believed that PBL is an effective, coherent, comprehensive, and concentrated
education approach. He implemented a questionnaire survey to 72 medical students
from Ludhianada Dayanand Medical College and hospital. Results showed that PBL
could facilitate independent subject learning and guaranteed a better ability in practicelearning and interest in innovation. Wong, D. K. P et al. [6] carried out a comparative
experiment on 132 sophomores majoring in social work who attended social work theory and practice and skill laboratory core courses under PBL. Results demonstrated
that PBL teaching could improve students’ cognition of social work knowledge, skills,
and values. Steele, D. J et al. [7] carried out a comparative analysis of influences ofWeb-based PBL teaching on learning outcomes of students. Results showed that using
PBL technology had positive effects on academic performances of students. Mulyanto,
H et al. [8] carried out an experimental study on 309 students and found significant
differences in math learning outcomes among students using PBL and the traditional
learning mode. Prosser, M [9] pointed out that differences among students in cognition154

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Paper—Influences of Problem-Based Online Learning on the Learning Outcomes of Learners

and understanding of PBL are vital to their learning methods and outcomes. Timor, A.
R et al. [10] carried out a comparative experimental study on 29 medical students andfound that PBL had very obvious influences on learning outcomes and learning motivation. Dupri, D et al. [11] found that PBL could increase cooperation among students
obviously and also improved learning outcomes significantly. The comparative experimental results of Khusaini, K et al. [12] showed that learning outcomes of students
using PBL had significant differences from those of students using traditional method.
PBL increased learning motivation of students. Reinsini, C. E et al. [13] pointed outthat using PBL could significantly improve learning outcomes of students in basic
repairing ability of a braking system. Based on at least 20 studies since 1990, Albanese,
M. A et al. [14] introduced how PBL influences learning outcomes of students majoringin a medical department. Stentoft, D [15] pointed out that PBL is a teaching methodthat fully supports interdisciplinary learning in higher education, and it is conducive toimprove students’ potentials in interdisciplinary learning. Khatiban, M et al. [16] compared effects of PBL and traditional clinical education. Results showed that comprehensive ability and unique ability of students using PBL were improved significantly
more compared with students in the control group. PBL trained ability, attitude, andperformances of nursing students. Goni, A. M et al. [17] analyzed influences of PBL on
learning outcomes of Grade 4 students. Results showed that after PBL teaching, mathlearning outcomes of students were improved obviously. Miller, S. K [18] analyzed
differences in performances and satisfaction of pharmacological students who adopted
PBL compared with traditional teaching mode. Results showed that both modes might
be course teaching methods with the same effectiveness. Aslam, L. K et al. [19] concluded that PBL could obviously facilitate improvement of skills of learners. Utami, I
et al. [20] applied PBL to teaching basic skills of volleyball passing, finding that in thefirst cycle, PBL increased the number of students skilled in passing. Winning, T et al.
[21] found that PBL can provide an incentive learning environment, promote comprehensive learning, encourage systematic patient management methods, and develop
independent learning skills. AlHaqwi, A. I [22] conducted a questionnaire survey of
174 undergraduates in medical school and found that PBL had positive effects on thedevelopment of their cognitive, individual, and team cooperation abilities. Teachers
were recommended to be equipped with professional knowledge about contents andprocess, so that learners can easily obtain the best outcomes from PBL. Dharma,
I. M. A et al. [23] conducted a comparative analysis of 68 students from two schools
by using one-way analysis of variance (ANOVA) and multivariate ANOVA analysis.
Test results indicated that PBL improved students’ ability in understanding the theme
easily and quickly during the learning process and it had some influences on learning
outcomes in social studies and critical thinking ability of students. Hmelo-Silver, C. E
et al. [24] found that PBL could decrease cognitive loads of learners and allow students
to improve their content knowledge, cognitive practices, cooperation, and independent
learning ability in complicated fields. McPhee, A. D [25] introduced the developmentof PBL and some of its applications and introduced possible uses of PBL in this field.
Our literature review indicates that the procedure of PBL has been extensively
approved and used in developed countries, such as those in Europe and America.
A consensus shows that PBL not only achieves good teaching effect but also improves
independent learning ability of students significantly and trains creativity, practiceiJET ‒ Vol. 18, No. 01, 2023

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ability, cooperative spirit, and communication ability of students, and finally improving their academic performances obviously. Additional studies have proved throughcomparative experiments that PBL is an excellent teaching method and has outstanding
teaching effects. Given that PBL emphasizes the dominant role of students in learning
activities compared with the traditional learning mode, the classroom is returned to students and it fully motivates the subjective initiative of students, so that students improvetheir comprehensive quality in analyzing and solving problems. Using PBL in specificsubjects can facilitate learning of students effectively. However, comparative experiments about PBL and learning outcomes might arrive at different research conclusions
in different subjects (e.g., physics, chemistry, geography, Chinese, history, and biology).

3

Methodology

3.1

Experimental objects

To test the effects of PBL on learning outcomes of learners, an experiment of
Cross-border E-commerce Customer Services for sophomore E-commerce majors
was carried out. Two groups were set: the experimental group that used PBL andthe control group that used the traditional teaching method. Students of both groups
were collected from Huanghuai University in Zhumadian City, Henan Province, withan average age of 20.3. There were 28 males and 12 females. To assure an average
homogeneous level of students in these two groups, all students had a mid-term exam
in Cross-border E-commerce Customer Services before the official implementation of
the experiment. Male and female students were grouped uniformly according to test
results. The experimental group (one class) and control group (one class) each had
20 students. The differences between the experimental group and the control group interms of means of various items did not reach statistical significance. Both groups were
homogenous. This experiment eliminated influences of physical and psychologicalconditions of students and assured authenticity and reliability of experimental results.
3.2

Experimental steps

First, two classes were grouped. The mid-term exam scores (covering weeks 1–8of the course) were used as pre-test results for an independent sample-T test. Pre-test
results showed no significant difference. In other words, the classes with equilibrium
levels were used as experimental and control classes thus determining the experimental
and control groups, respectively. In the second half of the learning period (weeks 9–16),
teachers adopted PBL officially with the experimental group, while the control group
continued to use the traditional teaching method. Subsequently, all students had a final
exam and their final exam scores were used as the representative of learning outcome
measurement. Later, a statistical analysis was conducted and a comparison test between
the experimental group and the control group was implemented. The final exam scores
were used as the post-test results for independent sample-T test, whose results wereused to verify the effectiveness of PBL. To assure accuracy of experimental results, theauthor controlled disturbance factors that may influence experimental effects uniformly
before the experiment, such as teachers, teaching duration, and homework.

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4

Results analysis

4.1

Comparison of pre-test results between experimental and control groups

The author set experimental and control groups, on which the PBL and traditional
teaching modes were applied, respectively. This requires students of two groups tomaintain a homogenous independent learning ability before the experiment to eliminate
influences of students’ differences in experimental results. The pre-test results (midterm exam scores, weeks 1–8) of students were compared before the experiment.
Table 1. Pre-test T-test
Groups (mean ± standard deviation)
Experimental Group (n = 20)

Control Group (n = 20)

65.75±2.7964.75±3.13

Pre-test results

t

p

1.067

0.293

Note: *p < 0.05, **p < 0.01.

Table 1 shows that differences between the experimental group and the control
group in terms of mean of various items not reaching reached statistical significanceaccording to the T-test of pre-test results. In other words, the P value was higher than
0.05. Both groups were homogenous, enabling the elimination of disturbances of physical and psychological conditions of students on experimental results. This laid a solid
foundation to guarantee objectivity of experimental results.
4.2

Pre-test and post-test paired sample T-test
Table 2. Paired sample T-test results
Pairing (mean ± standard
deviation)

Name

Difference(Pair 1 − Pair 2)tpPair 1

Pair 2Pre-test results of experimentalgroup + post-test results ofexperimental group

64.35 ± 2.52

81.30 ± 6.04

−16.95−11.3670.000**

Pre-test results of control group +post-test results of control group65.85 ± 2.68

74.85 ± 4.32

−9

−9.0960.000**

Note: *p < 0.05, **p < 0.01.

Table 3. Effect size indexes of paired sample-T test
Name
Pre-test results of experimental group +post-test results of experimental group
Pre-test results of control group +post-test results of control groupiJET ‒ Vol. 18, No. 01, 2023

Mean
Difference

Difference
95% CI

df

DifferenceStandard Deviation−16.95−20.071– −13.829

19

6.669

−9

−11.071– −6.929

19

4.425

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Generally, differences in experimental data were investigated by paired T-test.
Tables 2 and 3 show that the paired T-tests between the pre-test results and post-test
results of the experimental group all indicated differences (p < 0.05). The difference
between pre-test results and post-test results of the experimental group was significant
on the 0.01 level (t = −11.367, p = 0.000). According to a detailed comparison ofdifferences, the mean pre-test result of the experimental group (64.35) was significantly
lower than the post-test result (81.30). Paired data all presented differences, indicatingthat PBL improved final-exam scores of the experimental group significantly. Thisis mainly because PBL increased the real effective teachers–students interaction andstudents–students interaction in the online classroom. PBL also brought diversified
teaching methods for online teaching and stimulated learning efficacy of students.
Students can show more active, positive, and engaged learning state than in a traditionalclassroom, and their learning outcomes are relatively obvious.

Test scores

100

80

60

Post-test resultsof control group

Pre-test resultsof control group

Post-test resultsof experimental
group

Pre-test resultsof experimental
group

Fig. 1. End-of-term scores of the experimental group were compared before and after

Figure 1 shows that after the final-exam scores of the experimental group were compared with the mid-term results, the average scores improved significantly. This indicated that PBL is an effective teaching method and it at least assured no negative effects
on final-exam scores of students. On this basis, horizontal comparison of final-exam
scores between the experimental and control groups was further carried out. The finalexam scores of the former were higher than those of the latter, but such a difference did
not show statistical significance. This might be because teachers have used PBL for ashort period (only for a half-semester, from weeks 9 to 16), thereby failing to develop

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its effects fully. The positive effect of PBL mode on academic performances of students
is also influenced by other extra variables. PBL could effectively facilitate the independent learning ability of students. Such improvement in independent learning ability is
not directly equal to improvement in academic performances. The academic performances of students are obviously influenced by extra student factors such as individual
knowledge basis, intelligence conditions, and degree of preparation for the exam.
4.3

Covariance test
Table 4. Covariance analysis results

Difference Source

Quadratic Sum

df

Mean SquareF

pIntercept

33627.992

133627.992

1804.997

0.000**

Groups

476.1

1

476.1

25.5550.000**

Gender394.9711

394.97121.2

0.000**

Residual

689.32937

18.631

Note: *p < 0.05, **p < 0.01.

Table 4 shows that gender was included in the model as a covariable for the covariance analysis to study the influences of PBL on post-test results and prevent disturbances of other factors on the model. Obviously, gender has influences on academic
performances. This might be because male students are more interested in professional
knowledge requiring manual operation when learning E-commerce, making them significantly better than female students.
Table 5. Non-parameteric test results
Gender, Median M (P25, P75)

Post-test
results

Males(n = 28)

Females(n = 12)80.000(75.0,86.0)

73.500(72.0,76.8)

Mann–Whitney Test
Statistical U-Value

Mann–Whitney Test
Statistical z-Value

p

64.5

−3.0670.002**

Note: *p < 0.05, **p < 0.01.

Gender differences in one item of post-test results were investigated by anon-parametric test. Table 5 shows that the Mann–Whitney statistical analysis wasused investigate differences between two gender groups (1.0, 2.0). Different gender
samples all showed significantly different post-test results (p < 0.05). According to
specific analysis, p value of gender to post-test results was significant at the 0.01 level(p = 0.002 < 0.01). According to a specific comparison of median differences, themedian of final-exam scores of male students (80.000) was far higher than that offemale students (73.500). According to the summary and analysis, Mann–Whitney
statistical analysis was applied. Results showed that different gender samples showedsignificant differences in post-test results. This might be because learning efficacy offemale students in the course was poor and they easily accumulated learning fatigue.
This conclusion also inspires university teachers to consider gender differences for

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relatively difficult subjects and even explore teaching reform of “one student, one
scheme” to improve learning outcomes of female students in engineering courses.

5

Discussions

In this study, the influences of PBL on academic performances of students were investigated through comparative analysis between pre- and post-test results of the experimental group. Results showed that PBL improved academic performances of the experimentalgroup significantly. This is enough to prove that such a new teaching mode can improve
the academic performances of students significantly. Possible reasons for this ability are
as follows. PBL emphasizes the dominant role of students and stimulates their learning
enthusiasm. Teaching E-commerce for undergraduates has long applied the cramming
teaching mode. Such a traditional teaching model often centers on teachers. It starts withteachers in the teaching process and controls each link of the teaching process but puts
students in the role of passive knowledge receivers. Psychological states of students, suchas initiative and enthusiasm, are not the core problems of concern in the whole teachingactivity. The primary task of the teaching process is to realize the teaching objective.
The spirits of exploration and curiosity students are compressed and ignored. PBL modechanges such a situation. Students again become the subject of teaching activities. They
are supported and encouraged to learn positively and actively and develop their own ability to solve learning problems that teachers set for them. In this process, the enthusiasm
and initiative of students are respected highly. By solving pre-setting problems, students
show their ability in problem solving, experience realization of individual values, further stimulate their interests in independent problem solving, and extend such emotions
consciously to activities out of the classroom. Thus, space is created to further develop
the independent learning ability of students. Final-exam scores of students are also an
important index to measure the teaching effect of a new teaching method. In this experiment, the average academic performances of the experimental group have been improved
significantly, indicating that PBL is an effective teaching method. The researcher further
carried out a horizontal comparison of academic performances between the experimental
and control groups and found that the final-exam scores of the former were higher thanthose of the latter. However, such a difference did not reach the statistical significancelevel possibly because the external validity of research conclusions was relatively low
due to the small sample size. The PBL mode was also used for a short period (only fora half-semester) and the teaching effect of PBL mode had not yet been developed fully.
However, academic performances of students are obviously influenced by extra student
factors such as individual knowledge basis, intelligence conditions, and degree of preparation for exams. These variables will be considered and controlled in future studies.

6

Conclusions

China’s higher education is facing opportunities and challenges of online learningin the background of the COVID-19 epidemic. Owing to rapid scientific and technological development, teachers are empowered to make innovations in educationmode, drive changes in classroom teaching, and update philosophies of education and160

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Paper—Influences of Problem-Based Online Learning on the Learning Outcomes of Learners

teaching modes. These are important directions to improve online teaching effects. PBL
establishes the corresponding learning scenario based on real-world experience andadvocates student-oriented teaching. PBL is a way to provide teaching activities aroundcore problems. It can effectively solve the physical spatial separation problem between
teaching and learning in online learning and improve the learning efficacy of learners. In
this study, a comparative experiment was carried out based on the course Cross-border
E-commerce Customer Services for sophomore E-commerce majors at Huanghuai
University in Zhumadian City, Henan Province, China. Results show the following.
(1) The difference between pre- and post-test results of the experimental group is significant at the 0.01 level (t = −11.367, p = 0.000). (2) Academic performances of theexperimental group are significantly better than those of the control group, indicatingthat PBL is more beneficial to improve learning outcomes of students than traditional
teaching methods. (3) Significant gender differences are observed in post-test results(p = 0.002 < 0.01). The median of final-exam scores of male students is far higher
than that of female students. Suggestions for future researchers include expanding the
research scope, research levels, and research object types of PBL teaching experiments.

7

Acknowledgment

This study was supported by the Key Scientific Research Projects of Henan Province
Colleges and Universities (23A630036).

8

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9AuthorChi Zhang, Master’s degree, is a lecturer at School of Economics and Management, Huanghuai University. Her research interests focus on E-commerce and Teaching
Research. (email: 20121261@huanghuai.edu.cn).
Article submitted 2022-10-08. Resubmitted 2022-12-07. Final acceptance 2022-12-09. Final version
published as submitted by the authors.

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Paper—EEG Spectral Feature Markers as an Indicator of Human Cognitive Process

EEG Spectral Feature Markers as an Indicatorof Human Cognitive Process
https://doi.org/10.3991/ijet.v18i01.31259

Juris Porozovs1(), Viktors Veliks1, Aija Kļaviņa2, Gunita Praulīte1
University of Latvia, Riga, Latvia
Latvian Academy of Sport Education, Riga, Latvia
juris.porozovs@lu.lv
12

Abstract—Information technologies allow using modern and timely effective
analyses of EEG waves and the methods of data processing that allows effective
usage of this method into pedagogically and psychologically oriented research.
Aim of this study was to develop and validate method of EEG signal spectral
properties usage in the investigations of the process of cognition in the processof the perception of music by the choice of professional studies. 23 research
participants took part in the research – the students at the University of Latvia,
the division of participants “non-musician” and “musician”. The EEG recording
synchronized with the musical signal using the generated synchronization signal that given to one of the unipolar input channels of the EEG equipment. The
research analyses the basic rhythm of EEG the changes of the maximum frequency and the wave frequency power in the processes connected with the perception and cognition of music for 15 seconds long intervals. During the timeof listening to the chorus songs, the range frequency of the range rhythm ofalpha and beta does not change to the musicians but during the time of listening
to the instrumental music it increases but it was more vivid in the range ofbeta frequency. Non-musicians reacted differently – while listening to chorus
songs and instrumental music the frequency of alfa waves of EEG increased,
but the beta wave frequency decreased. EEG as a method of investigation isrecommended for pedagogical research to evaluate the neurological functions in
the cognitive processes.
Keywords—cognition, education, music, neuroscience, power of EEG
frequency

1

Introduction

Different studies have been carried out on the perception of music in recent years,
showing the mechanisms and functions of the brain involved in the processing of signals of a given modality. The interdisciplinary research in neuroscience, psychology,
biology, and music pedagogy made it possible to understand brain functions regarding
music. These data made a great contribution to modern music education. Results ofthese studies could be helpful to music educators and musicians for the study and communication about music [1].

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Nowadays there has been an increasing number of studies on music perception andperformance, and their correlation with processes taking place in the central nervous
system. The increase in the number of scientific papers on music is linked to the factthat research into music contributes to a better understanding of the structure and functioning of the human brain [2]. Furthermore, studies have shown that music education
helps students to develop learning abilities, improve their academic performance anddevelop a range of skills and knowledge that are essential for lifelong success [3]. An
approach for learning music with the intelligent classroom teaching system has beendeveloped. The teaching system of intelligent classroom has different modules, and thissystem should have managed by database system, which can help to assist and deepen
the teaching and understanding of music [4].
Music plays an integral part in forming the self-identity of adolescents and young
people’s as well as making their group identity based on their music preferences. Resultsof neuroscience studies allow producing the developmental model that explain the neural link between adolescents’ behaviour and brain development. The musical and social
choices are possible to explain based on the research of adolescents during brain development [5]. Learning of music is closely linked to memory and language. Memory
functions as interacting systems, which leads to encoding information, storing it, andmaking it available for retrieval. Nervous system gives ability to acquire different skills
and knowledge. Language plays a direct role in human cognition. Language allows to
combine meanings by constructing relational units and correlational networks [6]. For
acquirement of music important is also mobilization of different human functions toperform various tasks.
Many studies have shown the behavioural peculiarities of musicians and adolescentsduring music training in processing sounds and speech by changing signal properties(temporal and/or spectral). These effects are often associated with auditory cortex functional and structural changes, but it is oversimplified. However, it is necessary to movefrom the functions of local brain structures to neural networks and brain oscillatory
dynamics [7]. The human brain possibly contains different neuronal networks withspecialization for the processing of music. Brain specialization for music depends on
the recruitment of “free” neurons and development of their connections during music
lessons in the infants and adolescents’ brain. Music could modify neurons to suit theprocessing of certain information, and thus it is associated with the specialization ofneurons to perform certain functions. This type of specialization is closely linked tohuman culture and its impact on cognitive processes. The neural networks that are specific for music correspond to musical capabilities, and they are developing in different
people of the same culture. These abilities need to be universal in different cultures
and they help to develop musical competence. Depending on the nature of the music,
different neuronal networks may be involved in its perception [8].
Over the past decade, much experimental evidence has highlighted the importanceof automatically activated neural networks in the auditory cortex in musically significant forms of cognition [9]. The research suggest that the brain produces temporarily
individualized neuronal responses to the sounds of speech and music. These responses
are even stronger than the brain response to other natural sounds [10]. Music, like language, consists of a series of sequential events that are perceived by hearing and requirecomplex information processing [11]. Based on the results of the research a comparative model of neural networks that describe similar and different features of music andiJET ‒ Vol. 18, No. 01, 2023

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language was developed. Brown with colleagues considers that “the model assumes
that music and language show parallel combined generativity for complex sound structures (phonology) but distinctly different informational content (semantics)” [12].
Another model for research of music based on the assumption of participants classified
as musicians and non-musicians was used in different studies in which compares brain
functions in both groups. These studies show the role of music in the development of
human cognitive abilities [13]. Activation of neuronal patterns in response to music canshow whether a participant has music education [14]. The acquisition of music changes
the perception of hearing and the related organization of the brain, so it characterizes
the plasticity of the brain. Processes related to the perception of music are also connected with motor and auditory perception and speech. Musicians are better and fasterat the recognition of artificial language compared to non-musicians [15]. The results of
the research suggest that music training can transform the synchronization of the neuralnetworks involved in the processes of verbal memory [16].
Extensive information on the influence of music on the peculiarities of brain function and the role of different areas of the cerebral cortex in the understanding of music
has been provided using the electroencephalography method. An electroencephalogram(EEG) is a recording of the dynamic of the oscillations of electric potentials of neurons
in the brain. Spectral analysis of EEG signals helps to get important information about
the effects of music on the human brain [17]. EEG and Event-Related Potential (ERP)
methods are also used to study the relationship between computer language and neuralprocesses. It was found that the embedded system (programmable system which havea fixed functionality) leads to the changes in the electroencephalograms of computer
linguists [18]. Music stimuli induce motor system activities, and it has a powerful emotional trigger effect [19]. The analysis of working memory task between professional
pianists and control group people, showed significant differences in EEG under different electrodes. These differences are due to the peculiarities of perception of words
and sounds. Significant differences were observed also for the iconic memory tasks in
the right hemisphere of these two groups [20]. EEG recording of frontal and parietal
brain lobes while listening to music showed the importance of these lobes in music
perception [21]. It was found that EEG high frequency beta and gamma waves carryinformation about musical mode [22].

2

Methods

2.1

Participants

The study was performed with 23 volunteers (ages of 19–24) from the University of Latvia (LU), Faculty of Education, Psychology and Art. Two experimentalgroups were recruited, “musicians” from students of musical pedagogy program and“non-musicians” from students of other study programs without any active musicalrelationship. Musician group contained 12 participants, from them there were 8 females
and 4 males (mean age of respondents was 21.83 ± 2.48 years). The non-musiciangroup contained 13 female participants (mean age 23.92 ± 5.54). The experiment was
performed in a special quiet room and during recording EEG participants assumed a
comfortable position in a chair, during experiment light was turned off.

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2.2

ProcedureMusic stimuli in the present study were compiled from different genres – 3
representing Latvian chorus songs and 3 representing orchestral music. The first minute of each music’s sounds was recorded on one audio file mixed with silence in suchmanner – silence 1 minute, then stimulus 1 minute (see Figure 1), for data analysis
15 seconds EEG recorded intervals before and during stimulus was used. For auditory
stimulus presentation Observer XT version 10.5 (Noldus Information Technology) and
RealPlayer (RealNetworks, Inc) software’s were used for purpose of the audio signal
synchronization via unipolar input channel on the EEG headbox. Participants heard thesound in the ears loudspeakers (Samsung). After the experiment was completed, theresearch goal was explained to participant, if he (she) has questions. All procedures had
been accepted by the local LU ethical review board.

Fig. 1. Diagram of the experiment and data analysis
Notes: S1 – S7 – silence, the 60s; C1 – C3 – chorus songs, 60s; M1 – M3 – instrumental music, 60s; WN –white noise, 60s. A – data of the recorded EEG for further analysis, 15s.

2.3

EEG recordings and data preprocessing

EEG was recorded with a sample rate of 512 Hz. Cut-off frequencies were0.1–100 Hz. 19 standard electrodes of the 10–20 system were used. Electrodes were
fixed to the scalp with electrolyte gel at electrode positions, and generally impedanceswere < 5 kOhm. A ground electrode was placed at the forehead. Schwarzer EEG-29T
recording system with Coherence version 6.1.3.417 application software (Natus EuropeGmbH) was used.
Recorded EEG data were analyzed offline using Matlab 2020a (The MathWorks,
Inc) using EEGLAB (http://sccn.ucsd.edu/eeglab) with some custom scripts. The scalp
EEG was re-referenced to the computed average reference. 50 Hz noise in EEG signals was rejected using bandpass filters with value 48–52 Hz. Then EEG signals withperformance errors or remaining artefacts exceeding ± 100 μV in any channel were
rejected using ICA procedure (based on online EEGLAB tutorial https://eeglab.org/
tutorials/06_RejectArtifacts/RunICA.html) from data before processing. All cleaned
EEG trials were inspected visually before further computation. EEG waves spectral
properties were calculated with standard EEGLAB function SPECTOPO and thenpower was calculated for each wave’s bands using the handwritten script in Matlab.
Data statistical analysis (ANOVA) were performed using Statistica (TIBCO SoftwareInc., data analysis software system, version 13, http://tibco.com).

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3

Results and discussion

The analyses of the basic rhythms of EEG – delta, theta, alpha, beta and gamma
frequency ranges and power in the processes connected with the perception and cognition of music for experimental groups was carried out. In this study, the changes in the
wavelength ranges of the maximum frequency and the wave frequency range power,
which is calculated as the integral range was analysed. Multiple factors statistical analysis was performed in the three-way repeated-measures analysis of variance (ANOVA).
Factors selected for analysis were – the first factor groups” with two levels – musician’s
vs non-musicians, the second factor EEG waves with five levels – delta, theta, alpha,beta and gamma, the third factor – stimulus with four levels – silence before the chorus,
silence before music, chorus and instrumental music.
3.1

EEG spectral analysis ANOVA

For analyses, the following classically connected with perception and cognition ofmusic and language regions of interest (ROI) were selected – Broca’s area with twoelectrodes F7 and F3 (left hemisphere) and opposite (right) hemisphere electrodes F8
and F4, Wernicke’s area with T5 and P3 as well as two opposite side T6 and P4 electrodes and auditory cortex areas with T3 and T4 electrodes.
In both lexical regions (Broca’s and Wernicke’s area) and auditory cortex first –Groups factor has statistically significant influence F = 5.76, p < 0.001 (see Table 1)with higher Broca’s area dominance comparing both zones. The second finding fromANOVA was that in all ROI two factors contribution – Groups and EEG waves are statistically significant, with prevalence into sound processing area F = 6.39, p < 0.01 (seeTable 1). This indicates that musicians and non-musicians processed audio stimulus ina different way. More detail data analysis (mean and standard deviation) is described
below where each ROI is analysed (see Figures 2–4).
Table 1. ANOVA Tests of significance for experimental factors (groups, EEG waves, stimulus)
for Broca, Wernicke and auditory cortex ROI
Factors

ValuesF

Effects Df

pIntercept

0.51

139.87100.001Groups

0.96

5.76

10

0.01

EEG waves

0.45

32.53

40

0.01

Stimulus

0.98

0.90

30

0.62

Groups*EEG waves

0.84

6.39

40

0.01

Groups*Stimulus

0.98

0.74

30

0.85

EEG waves*Stimulus

0.94

0.76

120

0.98

Groups*EEG waves*Stimulus

0.95

0.59

120

0.99

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3.2

EEG wave band on the different ROI analysis

The main difference between two groups was on delta EEG band on the Broca’s area
in cases of instrumental music stimulus presentation with two electrodes F7 237712.2 ±31366.2 μV2 (mean ± standard error) before music and 90530.13 ± 31366.2 μV2 during
music listening in musicians group comparing with 02613.6 ± 30135.67 μV2 before
music and 43217.6 ± 30135.67 μV2 during music listening in non-musicians’ group,
same tendency observed in opposite hemisphere under F8 electrode 103946.01 ±12634.14 μV2 before and 146516.01 ± 12634.14 μV2 during music listening in the groupof musicians and in the group of non-musicians 41171.3 ± 12138.49 μV2 before and48821.6 ± 12138.49 μV2 during listening music. But in the case of the choral stimulus,
no differences between two experimental groups were observed (see Figure 2).

(a)

(b)

Fig. 2. Non-musicians vs musicians example of EEG waves spectral density under different
stimulus condition on the Broca area electrodes (a- chorus song, b- instrumental music)

In Wernicke’s area, the main differences between the two groups were on alpha EEG
band in all stimulus presentation cases in the non-musicians group. Also mean values in
P3 are higher than mean values in T5 in all groups, and this difference in non-musicians’
group are clearly presented (see Figure 3). For P3 in silence before chorus 51377.35 ±3545.36 μV2 for non-musicians, 31718.58 ± 3690.13 μV2 for musician’s and during
the listening chorus for non-musicians 48955.57 ± 3545.36 μV2, 35253.75 ± 3690.13
μV2 for musician’s, same in silence before instrumental 49294.1 ± 3545.36 μV2 fornon-musicians, 35949.9 ± 3690.13 μV2 for musician’s and during listening instrumental
music 49633.33 ± 3545.36 μV2 for non-musicians, 27773.77 ± 3690.13 μV2 formusician’s groups. Also, under electrodes, P4 and T6 non-musicians group showed
higher values than musicians.

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(a)

(b)

Fig. 3. Non-musician’s vs musicians example of EEG waves spectral density under different
stimulus condition on the wernicke area electrodes (a- chorus song, b- instrumental music)

In Auditory areas, main differences between the two groups were on beta EEG
band in all stimulus cases and observed values are higher in the non-musicians’ group
under T3 electrode (see Figure 4). For silence before chorus 25715.83 ± 3252.51 μV2
in non-musicians and 8136.88 ± 3385.32 μV2 in musicians and during listening chorus
27561.97 ± 3252.51 μV2 and 7679.93 ± 3385.32 μV2 in both groups. The similartendency under instrumental music, 16319.91 ± 3252.51 μV2 and 8392.84 ± 3385.32
μV2 before the stimulus and 17311.94 ± 3252.51 μV2 and 9046.22 ± 3385.32 μV2 innon-musicians and musicians’ groups. Interesting that under sound stimulus (choral or
instrumental) musicians group showed higher values in delta waves band under bothelectrodes – T3 24627.57 ± 3385.32 μV2 in the choral 30517.85 ± 3385.32 μV2 in
instrumental music listening in comparison with non-musicians 15056.09 ± 3252.51
μV2 in the choral and 19044.78 ± 3252.51 μV2 during listening instrumental music andT4 23943.54 ± 2629.95 μV2 chorus music 25047.25 ± 2629.95 μV2 instrumental formusicians’ group against non-musician peoples 17513.51 ± 2526.77 μV2 choruses and20325.78 ± 2526.77 μV2 instrumental music. Comparing power values of the electrodes
for musician T4 beta band is higher but for non-musician T3 values are higher and thisdifference is presented.

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(a)

(b)

Fig. 4. Non-musician vs musicians example of EEG waves spectral density under different
stimulus condition on the auditory area electrodes (a- chorus song, b- instrumental music)

3.3

Discussion

Recent scientific studies have shown that Broca’s area (which functions are related tothe speech) is structurally and functionally heterogeneous area of the brain. Some partsof Broca’s area share neurocognitive functions, while others are connected to otherareas of the brain and are involved in other functions. From the opinion of Fedorenko
and Blank there is a fundamental difference between the two sub-regions of Broca’sarea, which probably play different roles in cognition: the first is related to language
and the second to various other functions and their coordination. It is possible that these
two regions of Broca’s area carry out linguistic and cognitive processing in different
ways [23]. Musicians have been found to have a higher sensitivity index in a certain
Broca’s area.
In our study main differences between groups in delta waves, diapason was observed,that possible indicate higher remembering” activity during listening to music in musicians’ group in comparison with non-musicians.
Elmer group source-based EEG study with ROI over inferior parietal lobe andBroca’s area suggest that musicians have better sensitivity index (d’) during auditory
presented pseudowords. This change goes parallel with increased theta waves coherencein the left hemisphere. Non-musicians had higher functional connectivity in the righthemisphere. There were no differences between both groups during a passive listening
control or in the rest condition. These results show task-specific differences between
musical perception and word learning [24]. The literature suggests that musicians differ from non-musicians in several specific skills, which may range from right-hemisphere expansion in novices to left-hemisphere dominance in music professionals. Well
known that musicians have stronger bilateral neural connectivity and brain plasticity
caused by extended musical training, and there is a difference between recognition of
the music and other auditory stimuli like the voice in comparison with non-musicians.
Sound discrimination ability, as well as musical memory, seems to be distributed with aprevalence in the left hemisphere, but both hemispheres are involved [25].

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In our study, it was observed that in Broca’s area – musicians have a higher asymmetry
between left- (F7) and right- (F8) hemispheres in case of listening to instrumental music
in delta waves diapason. In Wernicke’s area opposite picture was observed – non-musicians have higher hemisphere asymmetry in both electrodes of the left hemisphere – T5
and P3, and for the right hemisphere in the T6 and P4 electrodes in alpha waves diapason. The same findings were observed in auditory ROI – non-musicians have higher
asymmetry in beta waves diapason in comparison with musician group during listening
to different types of music.
The results of the research of Koelsch group indicate that different chord-sequences(consisted of five chords, which were specifically mixed) were differently recognized
and separated in Wernicke area according to the chord’s specifics [26]. This indicatesthat processing of music is very similar to the processing of speech and similar cortical
networks that consist of front-lateral, anterior, and posterior temporal lobe structures
are involved in the processing of this information. This result supports the point of viewthat musical elements of speech play a significant role in the processing of language,
and both hemispheres are involved in this process.
Research has suggested that structural and functional brain plasticity results fromlong-term musical training, that produce differences in cognitive functions between
musical educated and non-educated persons [27]. The research has shown that musicians in comparison with non-musicians, have a faster response speed, a higher response
intensity, and a higher response power of beta waves [28]. The change in alpha rhythm
found in our study correlate with studies in binaural beat stimulation that can modulate
the strength of brain wave oscillations. The binaural beat is the presentation to every
ear of two sinusoidal tones with small frequency mismatch what yields an auditory
illusion of a beating. Brain frontal, temporal and parietal lobes are involved in theprocessing of binaural auditory beats. Alpha band network was modulated by low-frequency binaural beats and alpha oscillations seem to be involved in the perception of
binaural beat illusion [29].
The musician’s brain could be used as a model in the brain plasticity studies not only
in music perception but also in speech and related studies.

4

Conclusion

The peculiarities of the brain bioelectrical activity during listening to a music showthat musicians and non-musicians’ processes audio stimulus in a different way. The
main difference between musicians and non-musicians in the Broca’s area is in beta
EEG band. EEG values are higher in the non-musicians group. During sound stimulus (chorus or instrumental music) musicians group shows higher EEG values in delta
waves band under T3 and T4 electrodes in the choral instrumental listening in comparison with non-musicians’ group. EEG wave spectral analysis is an effective andreliable method for analyses the dynamics of neural processes in the brain during music
listening tasks.

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5

Acknowledgment

The research was done in the framework of the Latvian Council of Science
Fundamental and Applied Research Project No lzp-2019/1-0152 “Comprehensive
Assessment and Support Program to Reduce Screen Time Related Health Risks in
Adolescents”.

6

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Article e0129486, Jun., 2015. https://doi.org/10.1371/journal.pone.0129486

7

Authors

Juris Porozovs, professor, Dr. Biol. from 1993, in the University of Latvia. He iscurrently working at the Faculty of Education, Psychology and Art of the Universityof Latvia as a professor and leading researcher. The main research areas are neurophysiology (human cognitive processes, EEG studies) and pedagogy (peculiarities oflifestyle and physical activities of students). E-mail: juris.porozovs@lu.lv; ORCID:
https://orcid.org/0000-0001-7023-031X.
Viktors Veliks, researcher, Dr. Biol. from the University of Latvia (2007), currently
works at the University of Latvia. The main research area is neurophysiology – EEG
studies in field of cognition and memory. E-mail: viktors.veliks@lu.lv, ORCID: https://
orcid.org/0000-0002-4380-9534.
Aija Kļaviņa, professor, Ph.D. in Education. She is currently working at the Science
Department of the Latvian Academy of Sports Education. The main research areas are
physiotherapy and rehabilitation and pedagogy. E-mail: aija.klavina@lspa.lv.
Gunita Praulīte, asoc. professor, Dr.biol. (emeritus professor). The main research areas
are neurophysiology, sports physiology, and pedagogy. E-mail: gunita.praulite@lu.lv.
Article submitted 2022-03-29. Resubmitted 2022-11-13. Final acceptance 2022-11-15. Final version
published as submitted by the authors.

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Paper—Enhancement of Online Education in Engineering College Based on Mobile Wireless…

Enhancement of Online Education in Engineering
College Based on Mobile Wireless Communication
Networks and IOT
https://doi.org/10.3991/ijet.v18i01.35987

Jaafar Q. Kadhim1(*), Ibtisam A. Aljazaery2, Haider TH. Salim ALRikabi3

Electrical Engineering Department, College of Engineering, ALMustansiriyah University,
Baghdad, Iraq
2
Electrical Engineering Department, College of Engineering, University of Babylon, Bbylon, Iraq
3
Electrical Engineering Department, College of Engineering, Wasit University, Wasit, Iraq
jaafar80@uomustansiriyah.edu.iq
1

Abstract—The field of Engineering is that which needs a high level of analytical thinking, intuitive knowledge, and technical know-how. The area of communication engineering deals with different components including, wireless mobile
services, radio, broadband, web and satellites. There is a rapid decline in thequality of students produced by engineering faculties as a result of sufficientand quality methods and frameworks of student assessment. The production ofhigh-potential engineers is limited by the utilization of old and traditional education methodology and frameworks. The student presentation estimation system in engineering institution is a motionless manual. Usually, the assessmentof student’s performance using the traditional system is limited to the use of
students’ performance scores, while failing to evaluate their performance basedon activities or practical applications. In addition, such systems do not take cognizance of individual knowledge of students that connects to different activities
within the learning environment. Recently, engineering institutions have started
paying attention to evaluation solutions that are based on wireless networks andInternet of Things (IoT). Therefore, in this study, an automated system has been
proposed for the assessment of engineering students. The proposed system isdesigned based on IoT and wireless communication networks with the aim of
improving the process of virtual education. The data used in this study has beencollected through the use of different IoT sensors within the premises of the college, and pre-processed using normalization. After the data was pre-processed,
it was stored in cloud. In order to enable the classification of student’s activity,
an Adaptive Layered Bayesian Belief Network (AL-BBN) classifier is proposedin this work. The student’s scores have been calculated using fuzzy logic, while
Multi-Gradient Boosting Decision Tree (MGBDT) was proposed for decision
making. The use of python simulation tool is employed in the implementationof the proposed system, and the evaluation of the performance benchmarks wasdone as well. Based on the findings of the study, the proposed conceptual model
outperformed the existing ones in terms of improving the process of online
learning.

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Keywords—Internet of Things (IoT), Online education, Adaptive Layered
Bayesian Belief Network (AL-BBN), fuzzy logic, Multi-Gradient Boosting
Decision Tree (MGBDT)

1Introduction

One of the key contributors to national development is the educational sector, which
offers a wide range of learning locations and settings. The functionality and productivity of educational institutions have been significantly impacted on by the emergenceof technology [1–3]. A key purpose of global computing technologies is to expand theengagement of students. It also aims at collecting data from a wide range of sources
and incorporating them into different activity solutions, which is vital to the provisionof ratings that are based on the daily activities of students based on an educational
perspective. Regardless of the fact that there are huge technological advancements thathave been made in the area of education, the assessment of students is still carriedout manually. This is prone to human error as key features could be omitted when theperformance of students is computed. The IoT is an internet of networked, distinct
from others items that is gaining traction. It is basically aimed at developing intelligent spaces/environments as well as self-aware objects. The emergence of the IoT
has resulted in the alteration of the global computing [4, 5] asides the numerous areasthat surround different sensors. In more recent times, major new trends are emerging,particularly, in the area of combining sensors and device systems with technologicalsystems. This is coupled with the interactions between device-to-device and technological systems, which helps in solving most problems associated with device andprotocol. It is expected that the synergy between contextual data analytics and digitalapplications like machine-to-machine communications can promote the transformationof different industries. Also, it expected that the advancement of IoT innovation can beenhanced by the emergence of cloud computing as well as its use in the fog paradigm,
considering the increased utilization of smart products. The interest in this study ismotivated by these developments, stimulating the eagerness to examine extant work,
develop novel models, and unearth novel IoT applications. The advancement in online
education has been supported by the continuous technological advancement in the areaof wireless communication networks, which have attracted the interest of professionals and academics, and they’re already being used in nautical applications globally.
Worthy of note is the fact that the current situation has resulted in the forced transition
from face-to-face educational paradigm to online paradigm. Even though this paradigm is assumed to be simply embraced by learners, a closer examination shows that
this paradigm births a significant variation. The productivity of learners can be greatly
impacted on by changes made to mode of studying. Given that knowledge is owned byteachers, they are regarded as key players in the conventional learning system [6, 7].
Recently, there has been rapid advancement in the area of Mobile Wireless Communication Networks. The efficiency of wireless communication has been on the increase,which in turn allows for the deployment of several phases of cellular phone technologythat is currently used by numerous people [8, 9]. This trend began with 5G networkthat was majorly employed in voice conversations, but served as the foundation of all
mobile generations that emerged afterwards. In every phase, novel technologies are

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introduced, while new features and functions are supported. The basic overview ofInternet of Things is graphically represented in Figure 1 below.

Fig. 1. General representation of Internet of Things

Investigations on IoT (Internet of Things) technology have focused on determining
if it can be used in improving virtual teaching and learning. In the present study, anapproach that supports decision-making among administrators of educational institutions is proposed. It is expected that with this approach, they will be able to use dataobtained from the IoT to make well-informed decisions in the education sector. For
educational institutions to be able to make decisions based on data, they can make useof real-time data stream which can be analysed and used to feed their learning analytic
system. More so, the performances of the management of higher institutions are influenced by the ease with which academic platforms can be accessed through the internet.
The key contribution of the proposed approach is that, it demonstrates to educational
institutions that virtual teaching and learning can be supported and improved by using
data obtained through the Internet of Things. The application of IoT in educational
institutions is increasingly becoming popular, with institutions using the internet forthe collection, storage, and transmission of information [10]. The critical role of technology has been experienced in the education sector, especially in terms of student
connection and education. Virtual education has been significantly influenced by theInternet of Things, which has changed the conventional methods of teaching as wellas the architectural setup of educational institutions. The concept of IoT is regarded in
two-fold in terms of the roles it plays in virtual education, given that it can be used as atechnology for the enhancement of educational infrastructure, and it can also be studied
as a course [11–13]. The revolution of virtual education can be enabled at all levels of
education through the use of IoT technology. This technology offers great benefits to178

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different stakeholders at every level, including teachers and students. The use of Internetof Things has been employed in the areas of teaching and research. The incorporationof the Internet of Things into education allows the easy interaction between people andthings in the academia. Figure 2 is a representation of an IoT enabled teaching model.

Fig. 2. Internet of Things enabled teaching

There is still need for the further improvement of the wireless communications,
given the increasing need for connectivity between devices and the internet of things.
In the nearest future, the paradigms of IoT wireless communications network will be
needing approaches like dynamic spectrum access, spectrum sharing, optimal routing
and extraction of signal intelligence. The recent upsurge in the demand for IoT wirelesscommunications can be attributed to the unique nature of the Internet of Things wirelesscommunication networks coupled with the latest ubiquity of machine learning. Given
the high number of Internet of Things’ devices, huge interactions, especially throughwireless communication networks, it becomes important to have a networking architecture that is characterized by scalability. Particularly, the daily lives of humans aregradually being pinned around the IoT, giving room for unique access to information.
In addition, the IoT enables the improvement of the virtual learning, enhancing moreaccessibility. At the moment, improved results in learning are being recorded due tothe early efforts in IoT-based education. Learning and teaching resources have become
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wired network, thereby increasing the potentials of education for everyone. ThroughIoT-based learning, students and teachers in different parts of the world can be able toobtain local and international information that can improve teaching and learning outcomes. Virtual education is regarded as a promising alternative for the application of
IoT. Recently, the application of IoT technology has enabled the integration of instructional materials into the development of repositories that are scalable and rich in media
content. The subject of Internet of Things in education has been extensively studied.
The critical role of IoT in virtual education is illustrated in Figure 3 below.

Fig. 3. IoT in online education

Huge advancements in telecommunications, cloud technology, detectors, nanotechnology, and big data will be recorded because of the impact of IoT. Communication
between humans in different locations have been made possible and easy due to the presence of Internet of Things coupled with current trends in technological advancements.
Also, a great number of intelligent systems have been created with the help of IoT.
Quite a number of areas have been revolutionized because of the application of IoT
in those areas. One of those areas is higher education, in which improved learning,
experimentation, and management is being recorded due to the use of IoT [14, 15].
Thus in this work, a Multi-Gradient Boosting Decision Tree (MGBDT) algorithm
and Adaptive Layered Bayesian Belief Network (AL-BBN) are presented as ideas foraction development for optimal mobile wireless communication networks and IoT.
The remaining parts of the paper are structured as follows: Part II. Review of Related
Works and Problem Statement; (III) Proposed Work; (IV). Performance Analysis;(V) Conclusion

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2

Related works

In the study carried out in [16], the challenges of educational system as well as thepossible ways of addressing them through the use of IoT technology and were investigated the author examines educational system difficulties and how to fix them using Network infrastructure and IoT technology. In their work, they designed an algorithm thatenables a modern approach to learning, and named it “IoT-based Centralized Double
Stage Education”. The algorithm allows for contemporary technologies and instructional
strategies to be integrated. More so, the investigation of security challenges was done
considering the physical layer due to the fact that core network safety systems are characterised by complete privacy, minor computer complexity, consumption of resources, andgood adaption to change in channel. [17]. Their investigation focused on resource allocation, safety of intercept coding, processing of data, multi-node collaboration, alongside
identification and extraction of application layer key. The aim of this investigation is tofind ways through which the increasing security challenges can be tackled. The authors in[18, 19] beamed the flashlight on the present contributions of IoT to education, while
highlighting a wide range of obstacles that can hinder or challenge experimental endeavours in the future. This study was a review study that focused on the use of IoT in education, vocational learning, clinical advancement, green IoT, and wearable technology. In[20, 21], the researchers proposed an online education system (OES), which checkmatesabnormal behaviour of users. The system is able to flag consumers with unruly behaviour;
the system exposes the private information of such consumers. This is the strategy usedby the system to achieve system security. The aim of this is to make the user return toappropriate behaviour. The result of the system test based on reliability and safety parameters, the proposed system provides more security than existing ones. The security of thesystem enables stem education through the deployment of resource-constrained IoT
devices. It was also reported that only little latency is required for communication andcomputation. In addition, efforts were made by [22, 23] to enhance the process of learning; the researchers integrated Learning management systems with AI “artificial intelligence” so that the process of learning can be improved. As part of the new normal, this is
aimed at the creation of a standard educational shift that allows students to learn virtuallyand to be able to gain access to virtual assistants that can support their academic endeavours. The present level of research and practice in the area of engineering education was
evaluated in study [24, 25]. Who also investigated the implication of industrial research.
This was done considering the new trending services through wearable technologies,
mobile computing, deep learning, and internet of things. A discussion on the ideas andhistory of internet of things was presented in [26, 27] alongside the idea of educational
administration, education, and related issues. The application of such modern technologies can enable the identification of difficulties that exist within the education sector, andhelp decision makers in taking the right steps within a smart environment. To this end,
FHM University of Applied Sciences, a well-recognized education in Germany, wasinvestigated as a case study of an experimental implementation in the area of IoT. Anothereducational system was designed by the authors in [28–30], and the system was designed
to imitate the intellectual intelligence of learning things. It is a learning system that isbased on IoT, and designed to complement conventional methods of education alongside
top-notch learning approaches. The use of this system can be employed on a wide range
of applications and devices allowing interaction and opinion-sharing with consumers

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through the internet of things. The critical role of internet of things in enhancing the process of education, particularly learning was analysed by the authors in [31, 32]. In theirstudy, they highlighted the application of IoT in different areas of education, including,
distant learning, medicine, science, consumer green education amongst many others. The
emergence of the internet, transformations in terms of human-to-machine, human-to-human interactions have occurred, and these have been transformed to worldwide communications network. In [33], the author proposes a strategy for developing a long-term
educational environment that is financially, socially, and ecologically responsible. In
addition to technology, a better IoT-supported educational environment necessitates
increased collaboration among institutions, staff, and students. The vision of a completely
reformed field of education, digitally assisted, enriched, and financially, economically,and socially economic health is only conceivable with the full commitment of every
stakeholder and respective desire to help and team up. In [34], the use of blockchain technologies in education was systematically evaluated with the aim of providing in-depth
insight on the critical application and role of blockchain in education. They also focusedon highlighting its application in the future developments of the education sector. Blockchain technology has huge potentials in the area of education, and as such more efforts
should be geared towards exploring such potentials. These authors, through their research
have set the platform for decision makers and academics to further explore other areas inwhich this technology can be applied. The author in [35], proposed the use of a wide
range of sensor devices in an educational system. The proposed system was found to beefficient in terms of its scalability in addressing the increasing sensor populations. They
described the architecture and implementation of the IoT from a software perspective.
They also provided a description of the proposed system’s features, carried out an analysis of the lessons learned, while highlighting future trends of their work. The authors in[36] carried out an investigation of the basic principles, features, classifications, technologies, and challenges associated with the internet of things. In their work, the crucial role
played by IoT in the development of a smart educational system was demonstrated. They
also highlighted the contribution of IoT in decision-making by enabling sound judgement
and capacity building which are critical to the daily activities of humans. Also, major
utilities in different industries can be expanded and improved upon through the use of IoT.
By improving those utilities, applications can be developed through a new ecosystem bymeans of a world-wide distributed local wireless system of intelligent items. In the work
done by [37], a device referred to as wrist-based wearable for Education 4.0 (fitness
tracker) was proposed, and the benefits that can be derived from the use of wearable
device were highlighted. The authors also designed a questionnaire to evaluate the experiences of users in terms of their acceptance of modern electronic technology in higher
education. Their experimental results showed the key procedures and sensors for education 4.0. They also demonstrated through their results that their proposed fitness trackers
designed with a built-in sensor is capable of collecting a huge amount of real-time data
while kids are studying. The researchers in [38] show the importance of the current education technology in enabling the productivity of students and teachers. They also demonstrated that it is time saving for teachers, and requires less efforts. In this study, it was
revealed that the use of Learning Management systems helps students to acquire more
knowledge within a shorter period of time. It was noted in the study that, interaction
between teachers and students, teachers and teachers, and students and students is enabledthrough the proposed method. While showing the benefits that can be derived by182

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integrating an LMS with higher education, the authors also urged other higher institutions
to adopt this technology.

3

Problem statement

There is a wide range of devices that can be used by students to accomplish theiracademic goals and tasks, including computers, phones, and tablets. These devices
offer them the benefit of participating in classroom activities virtually. In addition, IoT
facilitates interaction and communication between instructors and students. Although
IoT devices may have the simplest applications, such as registering entrance and exit at
a security door, there more complex applications of such devices as they are designedwith more sophisticated components. Additionally, the transmission of critical data to
IoT systems can impact on all other things related to it in a negative way.

4

Proposed methodology

In the current work, a quality IoT strategy has been developed by using an Adaptive Layered Bayesian Belief Network (AL-BBN) alongside Multi-Gradient Boosting
Decision Tree (MGBDT) algorithm within the internal environment. The purpose ofthis strategy is to increase data that can facilitate online learning. Figure 4 below shows
the functional flow of this study.

Fig. 4. Schematic representation of proposed work

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4.1

Data acquisition and synchronization

Data such as students’ geographical location and their daily interaction are collected
through a data acquisition system. The data which is gathered enables the incorporation ofenergy-efficient sensors, intelligent systems, as well as other monitoring devices. A personal sensor network composes of GPS sensors, radio frequency identification (RFID),
and other IoT devices. The purpose of designing this system is to enable the collection of
students’ information, daily engagements of both staff and students, and their locations.
More so, both unstructured and structured data are collected from the personal sensor
network by the gateway. After the data is collected, it is forwarded to a cloud storage
database for further analysis. The features of the dataset are represented in Table 1 below.
Table 1. Personal attributes of student
S.no

Features

Explanation

1

SID

Student identification number

2

Name

Student name3Age

Age of the student

4

Sex

Male or Female
Permanent address of the student

5

Address

6

Family member nameName of the family member

7

Family member mobile number

Family member mobile number

8

Student data

Student previous performance information

4.2

Data Pre-processing using normalization

The selection of the target data should be made from the unprocessed collection offinancial data so that the performance can be boosted. It is important that this data be
prepared to enable its usability. After the target data has been processed, it is analysed
and then the results generated through the use of data mining methods. The purpose oftransforming data is to alter the pattern and types of characteristics. The pre-processingof data is a step that is crucial to data processing and extraction. There are chances thatdata which is falsified will be found among the semi-structured, structured and unstructured datasets that are unprocessed. Thus, it is necessary for data to be processed so thatit can be void of noises, and can be standardized. The elimination of noisy images fromthe dataset requires the use of image retrieval methods. The normalization of the dataset
may be carried out at the stage of pre-processing. The arithmetical representation of
D-count is given be Equation (1) as follows:D  [( N  ) / ]

(1)

Where, b denotes the information’s mean and t represents the standard deviation,
while D is represented as,
D

184

NN
R

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Here N is the specimen’s mean, and R denotes the standard deviation of the specimen.
Below is the representation of the random specimen:
Ds   0  1 N t  t(3)The defects that are depending on t2 are represented by t.
Ensuring that, as seen below, the defects should not depend on one another.
ln ~ Ull  p 12

(4)Here, l represents the random parameter.
Then the movements of the variables are normalised using standard deviation. The
momentary scale deviance is determined using the formula below (5).
NNR 

 nnr
nnr

(5)

Here, momentary scale is denoted by mms.  nnr  Va (n  ) ^ NNR(6)N represents random variable, while Va denotes predicted values.
nnr 

 Va  n   ^ NNR  ^ 2

(7)

nnr
N

(8)

lp =

The coefficient of variance is denoted as lp.
Each parameter should be fixed at zero so that characteristic scaling can be paused.
This approach is referred to as “unison-based normalizing approach.” After normalization, the formula will be as follows:N'(l  lmin )(lmax  lmin )(9)Upon completion of the process of data normalization, the data may be saved and thedataset’s anomalies and length can be maintained. This stage is aimed at minimizing or
eliminating data delays. Afterwards, the dataset that has been subjected to the processof normalization can be used as input data in the subsequent stages of the process. The
dataset for students’ virtual education is saved in the cloud, which is referred to as an
Infrastructure as a Service (IaaS) provider. Here, data is gathered from different sources
and can be retrieved at any time. The use of cloud storage repository is employed insaving the time-stamped data, and this data is later on retrieved for analysis. The classification of students’ activities is done appropriately based on the different hypotheses
highlighted in the categorization section. In addition, students’ personal information is

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saved in the cloud repository, where each student is assigned a unique number throughwhich they can be identified. The unique number is connected to the daily IoT-based
activities within the institution.
4.3

Students Activity Classification using Adaptive Layered Bayesian Belief
Network (AL-BBN)

In the methodology that is proposed in this study, there are two main categories ofactivities including, rare and monotonous activities. The later refers to those activities
that involve the daily monitoring of students’ progress using data from IoT sensors andhardware devices. The use of activity models can be employed in defining a wide arrayof students’ activities. In this work, the correlations between independent variables andstudent attrition (which is the dependent variable), are captured and described using
AL-BBN. AL-BBNs are complex mathematical models that show explicitly and naturally show the link prediction structural correlation across different models in an arrayof variables and variable groups. The AL-BBN chain rule is an approach that can beused to conveniently represent complex probability distributions, whereby each ix represents a variable and Tzix indicates the parents of the same (ix) variable.
T (i1 ,, in )  T (i |Tz )
n

x 1

x

(10)

ix

Some of the important activities of students that can be captured include participation of students in different forums and discussions, participation of students in study
cohorts, college involvement in interactions that are key to the theme, and terms ofmembership of physical activities. The Bayes rule is the conditioned stimulus, and thisrule involves the computation of the possibility of each provided target number or classof value. After the computation, the structure possessing the best estimated prediction
is selected for the next stage. Lastly, this replica is aimed at identifying provisional correlations between student attrition predictors. For a variable ix, a suitable representationof the students is required:Tzi  {B, i ( x ) }

(11)

x

Due to the fact that exponential growth is exhibited by the amount of conditional
estimations that must be created for each root, it becomes necessary for an intelligent
and professional AL-BBN engineer to commit a good amount of time to one or moredomain experts so as to be able to build a moderate-sized network manually. The example below shows that B serves as the base classifier in the second fall that has beenrecorded. These utilizing techniques how much material is supplied when the category
variable is known.
X T (ix ; i y ) 186

T (ix , i y ) T (i , i ) log T (i )T (i ) , x  yi x ,i y

x

y

x

(12)

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The ordering, which is determined by the relationship amongst each pair of variables, only includes edges amongst predictor variables. An edge’s weight in a graph isdetermined by mutual information which denotes the relationship between two variables. The following formula defines mutual information between two random variables ix = Fall student active and iy = Spring student active:
X T (ix : i y | B ) i x ,i y , B

T (ix , i y , B) log

T (ix , i y | B)
T (ix | B )T (i y|B )

xy

(13)

Where the tree is a function over Tzix is the set of parents for each iy, and B is the classvariable Tzix that has no students.
4.4

Students activity score based on fuzzy logic

There is a relationship between each illustration and a period. The data which provides details about the activities of students is known as temporal data. Dissimilar
activities-related sensory input is provided in the form of behavior metrics at different
timestamps. Through the process of fuzzification, a collection of terms and fuzzy language concepts are created based on the exact set of inputs provided. Subsequently, theuse of membership function is employed in creating a set of fuzzy logic, and followedby the establishment of the final inference. Intra-class deviation, Inter-class volatility,and feature-based time of each component for each sub-band are all fuzzy logic input
elements provided to the student’s activity. The sub-band specific feature subsets are
selected by using fuzzy logic with the most suitable classifiers.
e : S m → [0, h]n .

(14)

The following production rules guide the fuzzy logic:
Si : IFr1  y1  B1i ANDr2  y2  B2i ANDrn  yn  Bni THENclass  di , i  1,, S ,where I denotes the rule index, S represents the number of rules, BKi is a fuzzy term
characterizing the k-th quality in the i-th rule (k = 1, … , m); di is the resultant class,
R = (r1, r2, …, rn) is the binary vector of features, and line r1∧ rk shows if a feature ispresent (rk = 1) or absent (rk = 0) in the classifier.
There is no exact quantity at the timestamp axis, considering the fact that there
is difference in timestamp according to student. A tensor which is referred to as thestudent-activity data tensor (RgPs) has been designed with the aim of achieving this purpose. The mathematical representation of the daily RgPs of a student is given as follows:
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In the observation table, the class label is defined {(yp; dp), p = 1, z} as follows:class  dt , t  argmax{ i }(16)1i  n j ( y p )   Bi1 ( y p1 )  Bin ( y pn )   (y )(17)  (y )(18)

 t ( y p )  Si D classt t ( y p )  Si D classt
j

j

m

k 1

Bik

pk

m

k 1

Bik

pk

Where mBik ( ypk) denotes the fuzzy term Bik’s membership function value at point ypk.
1, IFd p  arg max ei ( y p ; , R)1i  n zF ( , R) 

0, OTHERWISE
z

p 1

(19)

Where e(yp; θ, R) denotes the output of fuzzy logic with fuzzy terms θ parameters
and R at point yp. The key challenge of designing a fuzzy logic is to ascertain the maximum of the function in space R and θ = (θ1, θ2, …, θD):
 F ( , R)  max jjj min     max , j  1, D Ri  {0,1}i  1, n

(20)

j
j
Where θmin, θmaxdenotes the upper and bottom bounds of each parameter’s fuzzy
logic, respectively. This is an NP-hard problem, and in this study a proposal is made toresolve the problem by dividing it into two tasks which are student activity and fuzzy
term parameter. Each activity (Zy), where y is the amount of actions from 1 to n, andthe students must complete t. The probability assessment of actions Zy at day f can be
expressed as Uyf, where 1 ≤ f ≤ t is the element of working days in a college term. Using
the formula below, a student’s score for each activity R (Zy) can be calculated: U
R( Z )
t

f 1

y

t

f
y

(21)

Activities that are of great significance are considered as monotonous activities andhave a Uyf score that is calculated on a regular basis. While on the other hand, the balance of the events set is made up of activities that occur occasionally.
The fuzzy logic algorithm is presented in Algorithm 1 below

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Algorithm 1: fuzzy logic algorithm
Step 1 Initialization
Initialize (Pop)Archive.add (Pop)Set Operators Prob (1.0)
window = Stagnation = 0
Step 2 primary loop
if the stopping requirement is not met,
Step 2.1 Parents’ choice
for x = 1 to |Parents| do
ifRandomDouble (0, 1) ≤ β then
Parents[x] ← TournamentSSD (Archive)
else
Parents[x] ← TournamentSSD (Pop)end ifend for
Step 2.Reprint
Operator ← Roulette (OpProb)
Of fspring ← Operator (Parents)
Consider your options (Offspring)
UpdateUse = OpUse (Operator)
window++
Step 2.3 Update on the Archive
if ! (Archive.add (Offspring)) then
Stagnation = Update Stagnation ()end ifStep 2.4 Invoke the Fuzzy Inference System (FIS)
if window == windowSize thenUpdateOpProb(OpUse, Stagnation)
Stagnation = 0 window
end ifStep 2.5 Population Update
NewPop ← Pop.add (Offspring)
fast non dominated sortSSD (NewPop)
P op ← RemoveWorstSolution (NewPop)
end while
Step 3 Output
return Archive

4.5

Decision making layer using Multi-Gradient Boosting Decision Tree
(MGBDT)The results obtained from the previous classification of decision tree are used intraining each decision tree. The linear nature of the Multi-Gradient Boosting Decision
Tree, makes the parallel training of the decision tree challenging. The second S2 tree
training optimization target is the sum of the first S1 decision tree’s outcome and theremaining S-value, and the method’s final outcomes are the sums of each decision tree’s
outcome. Equation (13), in other words:
Ŝ = S1 + S 2 + S3

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There are two loss functions possessed by the MGBDT that are often utilized for
iterative optimization as shown in Equation (13). Alternatively, the effect can be optimized instantly, whereas, the other alternative can be the optimization of the gradient’s decent significance. There is a difference between MGBDT and classic boosting.
If MGBDT is sub-optimized, the target is optimal. The booster is a resembling device.
The estimated value of the many samples i as a node average power, which may be
represented as Equation, is the integral gain μ of a node split (14), i  /ee(23)y 1 yConsequently, the node’s mistake can be written as Equation (15),
Error  (ix  v) 2

(24)

In the process of node division, it is compulsory to select the feature which has thehigh tear gain for segment, and the tear winning L is determined using the following
formula:L  Q  Qx

(25)Based on Equation (17), the difference can be thought of as a less function, or Qx:
Qx  (i   )  (i   )
n f

n

f

2

eP

e

P

2

(26)

Consequently, every splitting node problem will be focused on identifying a variable that is capable of increasing the split gain the most Q and Qx are separated andexpanded as in formula (18):L  sum 2f / F  sumP2 / P  sum 2 /  total (27)

sum2 represents the total of squares of all variations, as stated in Equation (18), where
sumf2 and sump2 are the total of the squares of all variants in the sub tree, respectively.
Consequently, only L needs improvement. As mentioned earlier, the tree nodes in eachtree node in the MGBDT are not together, rather, they are separated in isolation. Eachtrained forest is aimed at summing up the original trees and rate of presentation. The
information entropy can be calculated using the training set. The n class y = 1, 2, … n
is defined by Q, which holds the number of data samples. Qy is the number of data
samples.
Y(q1, q2 , qn )  190

 w log (w )
n

y 1

y

i

y

(28)

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E is the tree’s root, and E contains e values {e1, e2, … … ee} The training set S is
divided into subsets {S1, S2, … … Se}, with Sx denoting a specific subset and ex denoting
the value of e. Syx represents the element of samples in Sx that belong to Qy.
K (E) 

ex 1S1x  S2 x  Snx
Y(s1x , s 2 x ,s nx )S

Y(s1x  s 2 x s nx )   w log (w )
n

y 1

yx

2

yx

Gain (E)  Y(s1x  s 2 x s nx )  K(E)

(29)
(30)
(31)

The purpose of using MGBDT algorithm in this research is to enable decision-making
on the performance of college students’ performance. Factors such as data extraction,
data treatment, selection of feature, training model, and prediction of unknown data
have been prioritized. The MGBDT algorithm is represented by Algorithm 2.
Algorithm 2: MGBDT algorithm
Input: The training data set GϵSn, size of training set e=|G|, sample data ixϵG, x = 1, 2, …e, number ofregression trees N, the maximum depth of each tree F.

Output: Trained model Q  SPP  q z. , p  1, 2,.., Np

Initialization: Q = ø, p = 0, p is the current number of regression trees
While p<N: For all ixϵG, calculate the corresponding dx and lx according to
p
−1
p
=1
d p = ∂H / ∂ jx (i x ) −  jx − 
jp (i x )  and l x = ∂ 2 H / ∂ 2 jx (i x );Initialize the p th regression tree SPP = ø and the current depth of regression tree f = 0;
While f<F:
Tranverse every leaf node Ny in SPP, find the best split for each leaf node according toargmin D 2H DS2 (D H  DS ) 2
split* split  r L H LS
L H  LS

where DH i x N y , hd x and LH i x N y , hl x, and DS, LS are similar.

Split Ny into left child NHy and right child, NSy, then add them into SPP;
Traverse all the leaf nodes of SPP, calculate the predicted value of Ny;
Add SPP, into set Q;
Return Q.

5

Performance analysis

The newly proposed IoT and Adaptive Layered Bayesian Belief Network (AL-BBN)
models are functional to Selective Multi-Gradient Boosting Decision Tree (MGBDT)
algorithm applications, according to this section. The performance of the proposed
approaches was analysed using performance parameters like precision, recall, accuracy, and score. In analysing the performances of the models, they were compared withextant approaches like Naïve Bayes, Artificial Neural Network, Logistic Regression,and Decision Tree.

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Fig. 5a. Performance score computation using proposed methodology

Fig. 5b. Proposed system component based execution time (in seconds) for each day

Fig. 5c. Proposed system stability

Figure 5. (a) The proposed mechanism for calculating performance scores, (b) proposed system component-based execution time (in seconds) for each day, (c) proposedsystem stability. Figure 5a Ten students from the same class were graded based on

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their monthly performance over a period of three months. The performance of the firstmonth was done using a manual system of grading, while that of the last two months
was done using the approach proposed in this work. The effecting duration of numerous
components required for the computation of the daily scores of students’ performanceis presented in Figure 5b. From Figure 5b, it can be clearly seen that the time required
for the data mining stage is much longer than that of the recognition stage. Increase in
the number of dataset based on the increase in the percentage of students’ results in therelative shifting of the mean absolute value as shown in Figure 5c.

Fig. 6. Precision comparison

Precision refers to a quantity in decimal numbers that comes with the whole number
and is unrelated to the accuracy. Practically, the concepts of accuracy and precision aresynonymous, and as such, can be easily interchanged. The comparison of the proposed
techniques with extant techniques based on precision is presented in Figure 6.

Fig. 7. Recall comparison

A user holds recall when recalling a visit or repeating a sonnet after reading its title.
Here, a multiple-choice test was used given that is easier than an essay and more preferred by people. The administered test was based on recall remembrance. The result

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of the comparison between the extant approaches and proposed approach based on therecall parameter is shown in Figure 7:

Fig. 8. Score comparison

Figure 8: Score of comparison between proposed approach and existing approaches.
score 

tptp  0.5 ( fp  fn)

(32)

Fig. 9. Accuracy comparison

Accuracy provides the categorization with the required educational information.
Figure 9: results of the comparison between the proposed approach and extant approachbased on accuracy.
Ac 

194

(tp  tn)(tp  tn  fp  fn)(33)

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Fig. 10. Comparison between the predictions of teacher-based and student version

Figure 10 shows the results of the graphical comparison, with the two scenarios overlapping in three elements (research opportunity, e-learning, and hyper-connectivity).
The organizations opine that the variables of virtual education IoT ecosystem are significantly influenced by the Internet of Things. Based on the student’s view the mostcritical component in virtual learning is self-learning, however, it is not of much relevance in the eyes of the teacher. Also, the influence of IoT use in virtual educationon the issue of cooperation is another difference between the two perspectives. It isbelieved by teachers that the IoT enables effectiveness in terms of teamwork, and cooperation, but students do not value the Internet of Things in that regards.

Fig. 11. Impact of the IoT in online education

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Figure 11 presents the impact of IoT on virtual education. The findings revealed that
IoT enables excellent performance in teaching, with high percentage in comparison to
the disagreement and agreement. More so, the findings show that in terms of improvement in teacher communication, most respondents disagreed that the IoT enables
improved communication. Meanwhile, a high percentage of the respondents agree that
IoT enables teaching activities.

6

Discussion

Here, the usefulness of the approach proposed in this study is estimated in contrast
to the IoT that was previously mentioned for the given data. It was also found that themethod proposed in this study performs as good as that of global standard approaches
like ANN [39], NBC [40], DT [41], and LR [42]. In ANN [39], the selected projection
technique directly impacts on the connection’s performance. The ANN should first be
converted into numerical characters before it can be used to solve any problem. In the
study of [40], the NBC ‘zero-frequency problem,’ occurs anytime the lowest error is
assigned by the system to the predictor data whose category is not found in the dataset.
Therefore, a mild strategy should be used in solving this problem. In the study of [41],it was demonstrated that the logistic regression may be significantly reshaped by asmall piece of data, which can in turn increase insecurity for the system. In comparison
with other approaches, the estimation of a decision tree can be extraordinarily complex
durion. It takes a loger period of time to retrain a decision tree. The authors in [42]revealed that the key limitation of crucial limitation of logistic regression is the condition of proportionality between the logistic regression variables. Predictor accuracy(coefficient size) as well as connection direction are also revealed (positive or negative). However, the aforementioned limitations can be solved through the use of the
proposed technique. In this part, the analyses of the experiments have been presented
for MGBTDT algorithm and AL-BBN. Based on the evaluation, it can be concluded
that the proposed approach demonstrated superior performance in terms of IoT as compared to ANN [22], NBC [40], DT [41], and LR [42].

7Conclusion

The proposed system demonstrates an experimental configuration concentrating on
the AL-BNN in a IoT virtualized environment. The MGBDT algorithm was presented
to initiate, solve, and test the problem’s intents. Consequently, the results revealed that
improvements were achieved in the IoT method. The extant approaches include Naïve
Bayes Classifier, Artificial neural network (ANN), Logistic Regression, and Decision
tree (DT). Lastly, the performance parameters of the study are examined in comparison with those of other models with the aim of determining the most effective one.
In addition, the procedures of data migration needed to meet the requirement of the
future generations can be deployed.

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8

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9

Authors

Jaafar Q. Kadhim, Electrical Engineering Department, College of Engineering,
ALMustansiriyah University, Baghdad, Iraq.
Ibtisam A. Aljazaery, Electrical Engineering Department, College of Engineering,
University of Babylon, Bbylon, Iraq.
Haider TH. Salim ALRikabi, Electrical Engineering Department, College ofEngineering, Wasit University, Wasit, Iraq.
Article submitted 2022-09-06. Resubmitted 2022-10-26. Final acceptance 2022-10-27. Final version
published as submitted by the authors.

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

A Career Recommendation Method for College Students
Based on Occupational Values
https://doi.org/10.3991/ijet.v18i01.37127

Lei Wang, Yuanyuan Fu(*), Yingchao Zhang

Marxist Institute, Cangzhou Normal University, Cangzhou, China
fuyuanyuan81719@caztc.edu.cn

Abstract—The numerous recruitment information and the information
asymmetry between college majors and work posts make it difficult for college
students to seize the job opportunities that conform to their occupational values,
and the employment success rate is always at a low level. The effect of current
college student career recommendation systems is usually unsatisfactory, and theexisting systems haven’t fully considered the role of college students’ occupational values in instructing their employment. To fill in this research blank, thispaper studied a career recommendation method for college students based on
occupational values. At first, the paper proposed a collaborative filtering algorithm based on the features of collect students’ occupational values, introduced
a few features that can affect their occupational values, assigned weight values
to these features, and gave the method for determining the weight. Then, basedon the principle of the Kruskal’s algorithm (the minimum spanning forest), thispaper modified the K-Means algorithm and clustered the features of occupational
values. At last, the paper elaborated in detail the principle of generating career
recommendation results for college students based on occupational values, andused experimental results to verify the correctness of the proposed career recommendation algorithm.
Keywords—occupational values, employment, college students, clustering
analysis, collaborative filtering algorithm

1

Introduction

For college students upon graduation, the career choice is a big choice to make. Ifthey cannot master the effective job information or well prepare themselves for employment, then they would face a muddy choice when determining their career direction in
the future [1–12]. At this time, the occupational values are playing a significant role in
instructing the career decisions of college students who do not have a clear career goal,
especially in aspects of career expectation, career selection, job search, job training,and job satisfaction [13–19]. The numerous recruitment information and the information asymmetry between college majors and work posts make it difficult for college
students to seize the job opportunities that conform to their occupational values, andthe employment success rate is always at a low level, so it’s a meaningful work findiJET ‒ Vol. 18, No. 01, 2023

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

an objective, effective, and personalized career recommendation method for college
students based on their own occupational values.
Li and Xu [20] used an intelligent optimization algorithm to design and develop
a college student employment management system. They surveyed college students’requirements for employment services provided by career instruction center, summarized the required functions of employment management system, divided the system
according to user types, and designed the corresponding system function modules. To
figure out the trends and status of college students’ employment, Qi [21] proposed a college student employment data clustering and mining method based on feature selection,and used it to analyze the employment market of college students and gave the employment data structure; then on this basis, the employment data of college students were
subjected to normalized compression, and the sparse scoring method was adopted fordata feature selection based on the preprocessed data of college student employment;
after that, the online clustering algorithm was adopted to conduct deep mining and clustering on the employment data of college students. To cope with the uneven distribution
and poor accuracy of employment resources, Qi [22] applied social network mining to
the design of the employment resource allocation algorithm for college students, theauthor constructed a long-term evolution system and performed interference suppression on it using inter-cell interference randomization technology, inter-cell interference cancellation technology, and inter-cell interference coordination technology, thenexperiment was carried out to prove that the proposed method can optimize the allocation of employment resources to a certain extent with shorter task scheduling time andhigher resource allocation accuracy and efficiency. Bulankina et al. [23] reviewed existing studies and the axiological approach, in their paper, the authors considered the strategies of pluralistic concepts of socio-culture spaces in post-modernism, and discussed
the axiological ideas supporting the value spheres of teachers and foreign language
teachers. Chang and Lin [24] discussed the role of career development motives in therelationship between job values and job satisfaction. They sampled administrators offour private science and technology universities in northern Taiwan, the 242 effective
data attained from questionnaire survey were then analyzed by a structural equation
model, and the results suggest that work values have a significant impact on careerdevelopment motives and job satisfaction.
Most of the existing college student career recommendation systems can hardly gain
an ideal effect and they fail to fully considered the role of collect students’ occupational values in instructing their employment. To fill in this research blank, this paper
attempts to explore a career recommendation method for college students based on
their occupational values. In the second chapter, this paper proposes a collaborative
filtering algorithm based on the features of college students’ occupational values, introduces a few features that can affect their occupational values, assigns weight values tothese features, and gave the method for determining the weight. In the third chapter,
this paper modifies the K-Means algorithm based on the principle of the Kruskal’salgorithm (the minimum spanning forest), and clusters the features of occupational
values. In the fourth chapter, this paper elaborates on the principle of generating career
recommendation results for college students based on occupational values, and uses
experimental results to verify the correctness of the proposed career recommendation
algorithm.

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

2

Career recommendation model and feature weight ofoccupational values

The process of recommending suitable employer units for college students is tosearch for companies and job positions with the highest similarity to their career goals.
Figure 1 gives the principle of conventional career recommendation for college students. Here, this paper proposes a collaborative filtering algorithm based on the feature
attributes of college students’ occupational values. A few features of the said occupational values and their corresponding weights of influence are introduced. The firstthing is to calculate the distance between the features of college students’ occupational
values; then, through clustering analysis, the nearest employment set of college students that fits the features of their occupational values is found; at last, in the set, career
recommendation is given according to the scores of employer units based on college
students’ occupational values. The proposed career recommendation algorithm generates neighbor sets with similar features of college students’ occupational values, thenlooks for students with similar occupational values, career goals, and job post interestsfrom the neighbor sets of similar features of occupational values, so the career recommendation quality is good.
The data form of the constructed career recommendation model is described as follows: assuming x represents the number of college students searching for jobs; y represents the number of different types of employer units; S represents the set of scoresof different-type employer units given by college students based on their occupational
values, wherein S = (sij)x×y and sij represents the score of a type of employer unit j given
by college student i; in this paper, the scores are divided into six levels, that is sij ∈{0, 1,
2, 3, 4, 5}, wherein the score level of employer units that do not conform to the occupational values of colleges students is rated as 0, and the score level of those that conformvery much to the occupational values of colleges students is rated as 5.
SimilarStudent 1

Student 2
Choos

Ch

e

Re

e

oos

Ch

Choose

e

oos

Ch

se

oo

ChooseStudent 3

end

m
com

Employerunit 1

Employerunit 2

Employerunit 3

Employerunit 4

Fig. 1. Principle of conventional career recommendation for college students

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

After that, the scores of the employer units are clustered, according to the Pearson
similarity, the college student who has the most similar score with the target student u isfound out, the specific calculation formula of similarity degree is given below:
PVC  sim(v, u ) iI vu( svi  sv )( sui  su ) /

iI vu( svi  sv ) 2



iI vu( sui  su ) 2

(1)

Assuming: NNES(v) = {v1, v2,…,vl} represents the set of nearest neighbors (namely
college students hunting for jobs), and there is v∉NNES(v), l∈[1,n); PVC–sim(v, v1),
I ≤ l represents the similarity of the features of occupational values of college students
v and v1; ESv and ESu are the sets of scores of employer units given by college students v and u, and the intersection of the two is ESvu = ESv∩ESu; sv 
svi / I v and
su 





iIv

s / I u are the average scores of employer units given by college students v

iIu ui

and u; |ESv| and |ESu| represent the number of employer units.
Each feature of colleges students’ occupational values can affect their career path
after graduation to varying degrees, and the conventional college student career
recommendation algorithms fail to comprehensive consider the different weights ofthese features. To distinguish the influence process and importance of each feature oncollege students’ selection of employers, this paper measures the influence degrees ofthese features based on the rate of information gain and determines the weight coefficient of each attribute. The specific steps of the feature weights of college students’occupational values are:Assuming: R represents the set of college students hunting for jobs; m represents thenumber of types of their career path after graduation, denoted as Z = {R1, R2, ..., Rm};
Rj = Zj represents the number of the samples of college students’ career path; |R| represents the total number of college students in R; GU(Rj) represents the probability thata college student would take the j-th type of career path after graduation, and it meetsGU(Rj) = Zj /|R|, then, the information entropy of the employment system of college
students can be calculated by the following formula:F ( R)   GU ( R )log GU ( R )
m

j 1

j

2

(2)

j

Assuming: GU(Rj) is the probability that R belongs to career path type j; R contains
multiple feature attributes of occupational values; a feature Y has p kinds of values,
namely Y = { y1, y2, ..., yp}; if Y = yl, then it’s assumed that Yjl represents the number ofsamples belonging to the j-th type of career path, and GU(Rj|Y = yl) = Yjl /|Rj| represents
the corresponding probability of attribution; let Al represent the set of record set of
Y = yl, then the conditional entropy of Y can be calculated by the following formula:F ( Al )   GU ( R | Y  y )log GU ( R | Y  y )
m

j 1

j

l

2

j

l

(3)

Assuming: GU(Rj|Y = yl) represents the probability of the j-th type of career path
after graduation when Y = yl, then when the feature Y is met, the information entropy ofthe career recommendation system can be calculated by the following formula:204

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

F (R / Y )   GU (Y  y ) F ( A )
p

l

l 1

(4)

l

The following formula calculates the information gain:
Gain(Y )  F ( R)  F ( R | Y )(5)

The information gain rate of the system increases with the decrease of its information entropy, that is, the uncertainty of the career recommendation system for college
students decreases with the increase of the amount of information provided by Y. To
measure the importance of the feature attributes of college students’ occupational values, assuming: SG(Y ) represents the amount of information contributed by Y to thecareer recommendation system; SL(Y ) represents the amount of segmented informationof Y, then the formula for calculating the information gain rate GR(Y ) is:
GR(Y ) = SG (Y ) / SL(Y )(6)

The calculation formula of SL(Y ) is:
Split (Y )  GU ( R | Y  y )log GU ( R | Y  y )
p

j

j 1

l

2

j

l

(7)

The information gain of all the features of occupational values is summed, and theweight coefficient of the features of occupational values can be calculated by the following formula: ( A)  GainRatio( A) /3

 GainRatio( A)
k

a 1

(8)

Clustering of the features of occupational values

The features of college students’ occupational values can indicate their similarities
in terms of career goal, job selection, job hunting, job training, and job satisfaction
to a large extent. Introducing these features can break through the limits brought bymeasuring the similarity degree solely based on the scores of employer units given by
students. Assuming: anm represents the m-th attribute of the n-th college student in thevector, A = {A1, A2, …, Am} is the vector formed by the features of occupational valuesof each college student, then there is:
 A11

 A12
 A31


A
 n1

iJET ‒ Vol. 18, No. 01, 2023

A12
A22
A32

An1

A13
A23
A33

An1

 A1m 
 A2 m  A3m   Anm (9)205

Paper—A Career Recommendation Method for College Students Based on Occupational Values

Assuming: V = {Av1, Av2, …, Avm} and U = {Au1, Au2, …, Aum} represent then-dimensional and m-dimensional eigenvectors of college students v and u, then thefeature distance between v and u can be attained by the following formula: vu 

 a a
m

vl

l

2

(10)

ul

The similarity of the features of college students’ occupational values F-sim can beattained based on distance and similarity coefficient:F  simvu  1 / (1   vu )  1 / 1  a a
m

vl

l

2

ul





(11)

To enable the clustering analysis to get close to reality as much as possible, thispaper introduces the feature weight of college students’ occupational values ω ={ω1, ω2, …, ωL}, then, the feature distance of the occupational values of college students v and u after introducing the weight can be calculated by the following formula: vu 

  a a
m

l

l

vl

2

(12)

ul

The modified similarity of college students’ occupational values is:F  simvu  1 / (1   vu )  1 / 1 

  a a
m

l

l

vl

ul

2





(13)

To minimize the feature distance of the occupational values of college students witha same type of career path and maximize the feature distance of the occupational valuesof college students with different types of career path, this paper referred to the principle of the Kruskal’s algorithm (the minimum spanning forest) to modify the K-Meansalgorithm.
Assuming: R = {r1, r2, …, rm} represents the set of college students hunting for jobs;
A = {A1, A2, …, Am} represents the set of features of college students’ occupational values; rl, r2, …, rl represent the classes of output clusters; xl, x2, …, xl represent the cluster
center values of each class; l represents the number of clusters, then the execution flowof the modified K-Means clustering algorithm is:
STEP 1: College students u and v are taken as nodes that satisfy v, u∈R, then (v, u)represents the connecting edge between u and v, A represents the eigenvector of college
students’ occupational values, then by taking the feature distance of college students’occupational values as the weight value of each connecting edge, a weighted undirected connection graph can be created and denoted as QM = (U, O), a sub-graph withn nodes and no edges is the time-space of the initial state, which can be expressed as
QM0 = (u,{}).
STEP 2: Select the edge with the smallest weight, if the nodes of the edge are within
QM, then this connecting edge is put into QM, after that, select the edge with the smallest weight from the remaining edges, until both nodes of an edge are within QM, then206

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

the edge is put into QM, and all edges in QM are built into a loop. These nodes belong
to the same type of career path Ri, then they are deleted from QM; again, from theremaining edges, the edge with the smallest weight is selected until the types of allnodes have been attained.
STEP 3: Repeat STEP 2 until all nodes form L types of career path Ri, i ∈[k,l]. Assuming: |Ri| represents the number of college students in Ri, xm = Σr∈ri R/|Ri| represents thecenters of L types of career path; at this same, the generated L types of career path is theinitial number of clusters of college students hunting for jobs, and the corresponding
center of the career path types is the initial cluster center.
STEP 4: For college students r∈R, calculate their distance from each career pathcluster center, and divide those with the shortest distance from the career path cluster
center.
STEP 5: Assign the mean value of each type of career path to the cluster center of
each career path.
l
2
STEP 6: O r  xm represents the squared error criterion function,


i

rri

repeat STEP 4 and STEP 5 until O is 0, then the clustering terminates.
Figure 2 shows the flow of clustering algorithm for college students’ employment
features.
Pre-processing of score data
Eigenvector ofcollege student’s
occupational
valuesScoring information ofcollege students

Generate L
types ofcareer path

Attain thetypes of allnodes

Calculate the distancefrom college students toeach cluster center ofcareer path

Eigenvector ofcollege student’s
objective
preferences

Select the edge with thesmallest weight
All edges in QM
form a loop

Determine the set of thenumber of students graduatedin each previous year

Calculate the information
entropyCalculate the rate of
information gain

Divide the students with the shortest distancefrom the career path cluster center

The clusteringterminates

Fig. 2. Flow of clustering algorithm for college students’ employment features

4

Generation principle of recommendation results

Clustering the feature matrix of college students’ occupational values only classifies
students with different features of occupational values into different types of career
path, however, the types of the personal career path of college students can not be taken

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

as the only criterion for judging the career recommendation results. In the decisions of
the employment of college students, there’re other objective factors such as the viewpoints of the students’ families and friends. So, only by continuing to study the objective influencing factors of the employment of college students on the basis of the scoresof employer units given by students according to their occupational values, can we findthe best-matched employers for them and give the final career recommendations.
Score ofoccupational values

Score of objective
preference

Employer unit
information

Feature extraction

Job position feature
analysis

Eigenvector
construction

Job position feature model

Career recommendation
prediction
Similarity match withthe objective preference
scores of previous
college students

Preliminary recommendation
results

Set of previous college
students

FilterScore similarity sorting
Final recommendation results

Fig. 3. Generation flow of the recommendation results

Assuming: S = {S1, S2, ..., Sm} represents the vector constructed by the scores of college students’ occupational values; Sm represents the scores of different-type employer
units given by college students; snl represents the n-th college student’s objective preference score for the l-th type of employer units, then there is:
 s11

 s12
 s31


s
 n1

s12
s22
s32

sn1

s13
s23
s33

sn1







s1m 

s2 m 
s3m snm 

(14)

The ideal of the career recommendation algorithm proposed in this paper is basedon the sum of objective preference scores of all kinds of employer units given by target college students, the employer unit with the closest comprehensive score given by
previous college students will be recommended to the target student. Details of thealgorithm flow are given below:

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

Assuming: Svu = Sv∩Su is the intersection of the scores of all types of employer units
given by college students v and u; |Sv| and |Su| are respectively the number of comprehensive scores of Sv and Su; then, the comprehensive scores of employer units sv* and su*
given by v and u can be calculated by the following formulas:sv 

 s /Si 1 ci

v

(15)

su 

 s /S

u

(16)

Sv

Su

i 1 ci

The definition of the similarity between college students v and u is:sim(v, u ) 




iSvu

iSvu

( svi  sv )( su  su )( svi  sv ) 2



iSv

( sui  su ) 2(17)

Through clustering, after a target college student has been classified to a typeof career path with the most similar features of his/her occupational values, within
this career path type, the similarity between this student and the objective preference
scores of previous college students is calculated, then the scores are sorted according tosimilarity degree, and the employer units with the closest comprehensive score (given
by previous college students) is recommended to this target student. Figure 3 gives thegeneration flow of the recommendation results.

5

Experimental results and analysis

The reliability values of each variable of the features of occupational values are
given in Table 1. The Cronbach α coefficient of each variable (income and wealth,interest and specialty, power and status, freedom and independence, self-growth,
self-realization, interpersonal relationships, physical and mental health, environmental
comfort, job stability, pursuit of new ideas, and social needs) is 0.916, which meansthat the reliability values of these variables are all greater than 0.7, indicating that theconsistency of the evaluation items is ideal.

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

Table 1. Reliability of feature variables of occupational values
Number of the Item

Cronbach α Coefficient

Income and wealth

8

0.847

Interest and specialty

5

0.816

Power and status

9

0.802

Freedom and independence

23

0.936

Self-growth

9

0.825

Self-realization

5

0.819

Interpersonal relationships

8

0.937

Physical and mental health

2

0.748

Environmental comfort

7

0.853

Job stability

5

0.948

Pursuit of new ideas

6

0.884

Social needs

8

0.827

Total

12

0.916

Item

Table 2 shows the difference analysis results of the occupational values ofdifferent-type career path. The analysis is conducted from four perspectives of occupational values: the interest type, belief type, ideal type, economy type. For college
students graduated in different years between 2010 and 2020, there are significant differences in the influence of their occupational values on their career path after graduation. College students who pay more attention to ideals and performance, social
relationship and sense of achievement generally choose to become employees of public
institutions or work as civil servants; those who pay more attention to money tend toprefer directional employment or starting a business by themselves; and those who
pay more attention to the quality of life or interests and hobbies tend to prefer flexible
employment or become self-employed.
Table 2. Differences in occupational values of different career path types
Type of Career Path
Unemployed or starting abusiness by oneself

Directional employment

Public institutions employee
or civil servant

Self-employed or flexible
employment

210

2010

Interest
Type

Belief
Type

Ideal
Type

Economy
Type

4.36

3.08

3.13

3.69

2020

4.18

3.52

3.46

3.27

Difference–0.15**

0.02*

0.06

0.01

2010

4.72

3.69

3.25

3.18

2020

4.04

3.11

3.50

3.42

Difference

0.01

–0.03

–0.06**–0.04**

2010

4.25

3.41

3.62

3.19

2020

4.06

3.12

3.27

3.84

Difference–0.05**–0.16*

0.07–0.26**

2010

4.96

3.52

3.84

3.59

2020

4.36

3.60

3.49

3.25

Difference–0.02*–0.07***

0.03**–0.14*

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

Fig. 4. Comparison of recommendation diversity of different recommendation algorithms

Fig. 5. Comparison of college students’ employment satisfactionof different recommendation algorithms

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

Fig. 6. Comparison of recommendation errors for different employer unit rating items

Figure 4 compares the recommendation diversity of different recommendation algorithms. The reference algorithm 1 is a career recommendation algorithm based solely
on the scores of occupational values; the reference algorithm 2 is a career recommendation algorithm based solely on the scores of objective references. Results of comparative experiment show that the proposed algorithm that integrates the occupational
values scores with objective preference scores has achieved a higher recommendation
diversity, reaching 38% to 47%. Due to the processing the modified K-Means clustering algorithm, the difference between recommended employer units always stays at ahigh level. With the increase of the length of recommendation list, the recommendation
diversity of the proposed algorithm is higher than that of reference algorithms 1 and 2,
and its advantage is even more obvious in case of shorter recommendation lists.
Figure 5 compares the employment satisfaction of college students of different recommendation algorithms. Students participated in the experimental research could
choose between three options of “very satisfied”, “satisfied”, and “not satisfied”.
Among all the satisfaction feedback, the proposed algorithm has gotten a higher proportion of satisfaction, indicating that comprehensively applying occupational values
scores and objective preference scores to the career recommendation of college students is effective, and students’ satisfaction with the career recommendation has been
greatly improved.
This paper compares the recommendation errors of different employer units under
the conditions of 10 scores per person, 15 scores per person, and 20 scores per person(Figure 6). Although the recommendation error is higher under the conditions of less212

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Paper—A Career Recommendation Method for College Students Based on Occupational Values

student number and employer rating items, the advantage of the proposed algorithm isobvious still. The more the students, the more the rating items, the better the quality of
the recommendation results of the proposed algorithm.

6

Conclusion

This paper studied a career recommendation method for college students based on
occupational values. At first, the paper proposed a collaborative filtering algorithm
based on the features of college students’ occupational values, introduced a few featuresthat can affect college students’ occupational values, assigned weight values to thesefeatures, and gave the method for determining the weight. Then, based on the principle of the Kruskal’s algorithm (the minimum spanning forest), this paper modified theK-Means algorithm and clustered the features of occupational values, elaborated on theprinciple of generating the career recommendation results based on occupational values,
used experimental results to verify the correctness of the proposed career recommendation algorithm, and gave the reliability analysis results. In the experiment, difference
analysis was conducted on the occupational values of college students taking different
career paths after graduation and the corresponding analysis results were given. The
recommendation diversity and student satisfaction of different recommendation algorithms were compared. At last, this paper compared the recommendation errors under
the conditions of different employer unit rating items, and the results showed that themore the student number and rating items, the higher the quality of career recommendation results of the proposed algorithm.

7

Acknowledgment“How to build a New System of “Self-Hematology” for Rural College Students
returning home to “How to build a New System of “Self-Hematology “ for Rural College Students returning home to Start their own Business under the Background of
Rural Revitalization”. Supported by the 333 Talents Project” of Hebei Province in
2022. Project Number: C20221196.

8

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educational hypermedia system (AEHS-JS) based on the overlay modeling and Felder andSilverman’s learning styles model for job seekers. International Journal of Emerging Technologies in Learning, 15(8): 235–254. https://doi.org/10.3991/ijet.v15i08.10574[3] Dhar, J., Jodder, A.K. (2020). An effective recommendation system to forecast the best educational program using machine learning classification algorithms. Ingénierie des Systèmesd’Information, 25(5): 559–568. https://doi.org/10.18280/isi.250502

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Iraqi private universities: TIU-S in focus. International Journal of Sustainable Development
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employment policy based on text mining. Wireless Communications and Mobile Computing, 2022: 3812215. https://doi.org/10.1155/2022/3812215[6] Zang, X. (2022). Accessibility analysis model of college students’ employment education
resources based on mobile search algorithm. Journal of Interconnection Networks, 2146010.
https://doi.org/10.1142/S0219265921460105[7] Li, N., Feng, L., Zeng, S., Bo, H. (2022). Analysis on the regional spatial distribution of
employment of college students and evaluation of employment quality. International Journal of Emerging Technologies in Learning, 17(19): 242–258. https://doi.org/10.3991/ijet.
v17i19.34519[8] Wu, D. (2022). Prediction of employment index for college students by deep neural network. Mathematical Problems in Engineering, 2022: 3170454. https://doi.org/10.1155/
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https://doi.org/10.1155/2022/7173771[10] Chai, W. (2022). Analysis on the social environment of college students’ rural employment and entrepreneurship. Computational Intelligence and Neuroscience, 2022: 3475897.
https://doi.org/10.1155/2022/3475897[11] Du, H. (2022). Research on the data driven intelligent employment information service
system for college students. In International Conference on Computer Application andInformation Security (ICCAIS 2021), pp. 638–643. https://doi.org/10.1117/12.2637662[12] Gu, L., Chen, X. (2022). Study on discrete dynamic modeling of college students’ innovative
employment security mechanism under the environment of internet of things. Computational
Intelligence and Neuroscience, 2022: 4637180. https://doi.org/10.1155/2022/4637180[13] Hwangbo, B., Denise R.S., Cassandra, M. (2019). Developing a conceptual model of construction employees’ professional values. Proceedings of the 8th Research in Engineering
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with history to explore a persistent tension. Engineering Studies, 10(1): 38–65. https://doi.
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Systems.
[18] DeMonbrun, M., Brown, M. (2014). Emphasizing professional skills and professional values: Investigating the role of faculty’s gender and departmental gender diversity. In 2014
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in The Netherlands. Technology in Society, 39: 10–17. https://doi.org/10.1016/j.techsoc.
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system based on intelligent optimization algorithm. ACM International Conference Proceeding Series, October 21, 2021, pp. 7–11. https://doi.org/10.1145/3516529.3516531[21] Qi, M.B. (2021). Clustering mining method of college students’ employment data based onfeature selection. In International Conference on Advanced Hybrid Information Processing,pp. 105–115. https://doi.org/10.1007/978-3-030-94551-0_9[22] Qi, M.B. (2021). An algorithm of employment resource allocation for college students basedon social network mining. In International Conference on Advanced Hybrid Information
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between work values and job satisfaction. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, pp. 2291–2295. https://doi.org/10.1109/IEEM.2017.8290300

9

Authors

Lei Wang, female, ethnic Han, born on January 16, 1990; native place is Cangzhou city of Hebei province, China; master and lecture of Cangzhou Normal University
majoring in ideological and political education. Email: wanglei900116@caztc.edu.cn.
Yuanyuan Fu, female, ethnic Han, born on July 19, 1981; native place is Cangzhou
city of Hebei province, China; master and associate professor of Cangzhou Normal
University majoring in ideological and political education. Email: fuyuanyuan81719@
caztc.edu.cn.
Yingchao Zhang, female, ethnic Han, born on December 13, 1991; native place is
Luannan County of Hebei province, China; master and lecture of Cangzhou Normal
University majoring in ideological and political education, and the research on Sinicization of Marxism. Email: Zhangyingchao1108@163.com.
Article submitted 2022-10-01. Resubmitted 2022-12-09. Final acceptance 2022-12-11. Final version
published as submitted by the authors.

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

Prediction of Students Performance Level Using
Integrated Approach of ML Algorithms
https://doi.org/10.3991/ijet.v18i01.35339

Mirza Azam Baig, Sarmad Ahmed Shaikh(), Kamlesh Kumar Khatri,
Muneer Ahmed Shaikh, Muhammad Zohaib Khan, Mahira Abdul Rauf

Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan
sarmad@smiu.edu.pk

Abstract—In this paper, the efficacy of machine learning (ML) techniques
for predicting the academic success of students is investigated. In issues pertaining to higher education, as well as machine learning, deep learning, and its linkages to educational data, predicting student achievement is essential. The choiceof courses and the development of effective future study plans for students can beeasier with the help of the capacity to forecast a student’s success. In addition topredicting student achievement, it makes it easier for instructors and administrators to keep an eye on children so that they can offer support and integrate trainings for the greatest outcomes. In this study, we define the idea of predicting thestudent performance in education and its several iterations. We discuss a number of ML approaches, such as the Fuzzy C-Means, the Multi-Layer Perceptron(MPL), the Logistic Regression (LR), and the Random Forest (RF) algorithms,
for predicting student achievement in the classroom. The models for forecasting
student performance that are now in use and those that have been proposed in thispaper are carefully investigated. The paper examines different combinations of
the algorithms including FCM – MLP, FCM – LR, and FCM – RF, and provides
the detailed results of each combination. These strategies are assessed using
quantitative standards including accuracy, detection rate, and false alarm rate.
Keywords—Machine Learning (ML), Fuzzy C-Means (FCM),
Multi-Layer Perceptron (MPL), Logistic Regression (LR), andRandom Forest (RF) Algorithms

1

Introduction

Today, major emphasis is being made on predicting student performance as a resultof the relevance of that type of issue in the development of nations all over the world.
This is due to the fact that the educational process leads to the production of a generation
capable of leading the country and march towards development in all aspects of life.
The efficacy of academic institutions, which are in charge of shaping future generationsin accordance with the various stages of people’s lives in each nation, is also reflectedin the evaluation of students’ performance. As a result, one of the most urgent needs

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

that motivates governments to make significant efforts is to advance the educational
process. In this regard, the students’ performance can be monitored by using technological approaches such as the machine learning (ML) approach which can predict
the features of students’ performance. ML is a method for finding hidden information
by studying numerous data sources in fields including business, social, medical, andeducation. The more data there is, such as in large databases, the better the forecast [1].
Several ML techniques are in use right now, particularly in the field of industry. Someapproaches necessitate the use of supervised learning techniques, whereas others necessitate the use of unsupervised learning techniques. On structured or labeled data, thesupervised learning approach works. Beyond that, supervised learning applies what it
has learnt from past data to new data (new data). It gives the machine model training
data and forecasts whether it’s a circle or a square, for example. For subcategory classification and regression, this supervised learning method is used. Unsupervised learning
makes use of unstructured or hidden data and necessitates the machine being given adiverse set of inputs. Unsupervised learning evaluates data before classifying it; as aresult, we get different data in each group after classification, and each group is distinct
from the others [2].
In order to gather the essential information, it is possible to study the knowledge
provided by various educational data resources. A new field called Educational Data
Mining (EDM) was established as a method of identifying essential information [3].
Sharing statistics and integrating deep learning with exploration create the best learning
environment. The importance of EMD has rapidly increased in recent years because
of increase in data collected, according to educational data obtained from various
e-learning systems and the expansion of traditional educational systems. The abundanceof data across numerous domains and their relationships with one another demonstrate
the power of EMD. It is focused with the extraction of traits from vast volumes of data
provided by the institute to assist the educational process [4]. Academic institutions
work to create a student model that can predict each student’s characteristics and performance [5]. From those vast datasets, we need to classify the useful information andprocess it for predicting the desired parameters [6–7].

2

Literature review

The use of the ML approach to forecasting students’ performance based on their history and term exam results has proven to be useful in predicting different levels of performance. Using such ML algorithms, it is possible to anticipate which pupils are likely
to fail and as a result deliver a solution to students in a timely manner. It can also assist
the educational institutes in identifying high-achieving students and assisting in offering scholarships. Ioannis E. Livieris, et al. in [8] predict student success in mathematics.
The authors created an Artificial Neural Network (ANN) classifier. They discoveredthat the modified spectral Perry trained artificial neural network performs superior classification than other classifiers in their testing. S. Kotsiantis, et al. [9] studied the useof ML techniques in remote learning for the prediction of student dropout. This study
made a significant contribution since it was a trailblazer in the field of educational data

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

mining. Although ML techniques had been used in other fields, he and his colleagues
were the first to use them in an academic setting. Rather of using class performancedata, an algorithm was given demographic data and numerous project assignments.
Moucary, et al. in [10] proposed a hybrid technique K-Means Clustering and Artificial
Neural Network (ANN) for students pursuing higher education while learning a newforeign language as a method of teaching and communication. To begin, a neural network was used to forecast the students’ performance, and then the K-Means technique
was used to group them into a certain cluster. This clustering assisted instructors in
identifying a student’s skills throughout the early stages of their academic careers.
A data mining-based prediction model for student performance with a few characteristics called student behavioral features is introduced in [11–12]. Three distinct classifiers were used to evaluate the model including Nave Bayesian (NB), ANN, and Decision
Tree (DT). Ensemble approaches such as Random Forest (RF), Bagging, and Boosting
were utilized to increase the classifier’s performance. When behavioral elements were
eliminated, the model’s accuracy increased by up to 22.1%. After employing ensemble
approaches, the accuracy climbed to 25.8%. The authors in [12] recommended prediction model structure that provides characteristics that result in less variation in information acquisition. Because ML algorithms break down problems and solve them one ata time, it’s possible that when one condition fails, other crucial attributes are lost. This
has an impact on the model’s overall performance. To do so, the model must allow endto-end functionality. Shaymaa E. Sorour et al. in [13] used DT and RF to create an interpretable model. They used a technique called remark data-mining to forecast grades
and extracted rules. In reference [14], the authors used Neuro-Fuzzy to predict andclassify student academic success based on CGPA. Quadriet et al. in [15] used J48 toforecast the dropout rate of students based on their CGPA. The elements impacting thepupils’ performance were found by Christian et al. in [16] using the NB method. The
authors looked at the schooling, particular, induction, and educational data of students.
Muslihah Wook et al. in [17] anticipated academic achievement of pupils by using theclassification approaches such as ANN and DT. Furthermore, Shaleenaet et al. in [18]used DT classifiers for identifying and predicting dropout students ratio as well as a
discussion of the topic of class imbalance. Another study in [19] used the deep neuralnetwork (DNN) and ANN algorithms to improve the DL prediction model and implementation technique. V. Vijayalakshmi et al. in [20] adopted ML and DL techniques inthe domain of education system on student performance to test the performance of theirsuggested strategy.
Most of the reported techniques have achieved the results of predicting the students’performance with complex structures and the results still need to be improved. In thispaper, by examining both supervised and unsupervised learning methods, followedby the Fuzzy C-Means (FCM) clustering approach, and classification techniques i.e.,
Multi-Layer Perceptron (MLP), Logistic Regression (LR), and Random Forest (RF)algorithms, we predict the student’s performance based on education parameters. Different combinations of the considered algorithms are investigated. Most of the combinations justified improved accuracy, precision, recall, f1-score, classifier, prediction,and clusters in a rapid, precise, and accurate manner.

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3

Proposed methodology

This section describes the proposed method which begins with an algorithm, subsequently loads data from a database, and finally, prepares the information. The information is pre-processed, normalized, and standardized. The standardized approachworks by first normalizing data to convert floating frequency values to the same
(0.1543648795), then doing standardized analysis and calculating the result. Then we
use the Fuzzy C-Means clustering technique and the classification algorithms including
Multi-Layer Perceptron, Logistic Regression, and Random Forest supervised classification algorithms to design and create the target model. The sample size for the test is
25%, whereas the sample size for the implementation training is 75%. Figure 1 illustrates a flowchart that can assist in understanding the research activity and outlining
the job breakdown structure concisely. In this illustration, we initially retrieve datafrom the database to prepare data, then actually begin pre-processing the data in thedata pre-processing section using standardized data cleaning techniques, then work onclustering and classification implementing 75% processed training data and 25% test
data for model validation.

Fig. 1. Proposed method for students performance prediction

The proposed method consists of the following two subsections: 1) Pre-processing,and 2) Classification.
3.1

Preprocessing

In preprocessing phase, to obtain meaningful data, we clean the data and use it forclustering. As explained below, this is done in two stages i.e., data collection, and clustering by using the Fuzzy C-means clustering technique; and then further process thedata in the classification phase.

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Data collection. The dataset of the international high education students performance, has been collected from the Kaggle website, which had previously been utilized
by the researchers in [20]. The dataset contains a total of 480 tuples and 17 properties,
each of which represents a student’s performance metric. The dataset contains 305 maleand 175 female students sample records from international, with one target class (which
denotes each class’s status separately) and a total of 128 Low Class, 211 Medium Class,and 142 High Class. The actual international high education students performance dataset feature description is shown in Table 1 [21].
Table 1. Actual dataset for international high education students performance
GenderNationality

Place of
BirthStage ID

Grade ID

Section ID

Topic

M

KW

KuwaIT

lowerlevel

G-04

A

IT

M

KW

KuwaIT

lowerlevel

G-04

A

IT

M

KW

KuwaIT

lowerlevel

G-04

A

IT

M

KW

KuwaIT

lowerlevel

G-04

A

IT

M

KW

KuwaIT

lowerlevel

G-04

A

IT

Semester

Relation

Raised
Hands

VisitedResources

Announcements
View

Discussion

ParentAnswering
SurveyF

Father

15

16

2

20

Yes

F

Father

20

20

3

25

Yes

F

Father

10

7

0

30

No

F

Father

30

25

5

35

No

F

Father

40

50

12

50

No

Parent School Satisfaction

Student Absence Days

Class

Good

Under-7

M

Good

Under-7M

Bad

Above-7L

Bad

Above-7L

Bad

Above-7

M

Table 2 displays the preprocessed dataset that has been cleaned and standardized,
and prepared for the clustering and classification phases to predict the students performance. Moreover, Figure 2 shows the mixed data chart of the corresponding cleaned
data before clustering.

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Table 2. Preprocessed dataset for international high education students performance
GenderNationality

Place ofBirth0.276542

0.456038

0.595424

0.9761720.181578

Stage ID

Grade ID

Section ID

Topic

0.416909

0.675114

0.664516

0.959812

0.820159

0.329478

0.635595

0.804943

0.677397

0.132544

0.429730

0.063652

0.445259

0.907755

0.918585

0.755372

0.830483

0.088741

0.159844

0.527569

0.263223

0.741383

0.871822

0.210892

0.701391

0.766264

0.172061

0.103681

0.024694

0.419428

Semester

Relation

Raised
Hands

VisitedResources

Announcements
View

Discussion

ParentAnswering
Survey0.609305

0.522702

0.782175

0.892479

0.164947

0.646486

0.145420

0.169182

0.336317

0.587580

0.624576

0.543965

0.676087

0.104618

0.045972

0.292576

0.704262

0.028200

0.060382

0.371900

0.112741

0.152801

0.884067

0.992723

0.117836

0.414137

0.987809

0.355952

0.949947

0.700166

0.426451

0.145548

0.839285

0.519597

0.009649

Parent School Satisfaction

Student Absence Days

Class

0.667879

0.930874

0.996918

0.115494

0.212968

0.871445

0.355699

0.400581

0.200401

0.783681

0.098311

0.266751

0.763605

0.616193

0.617324

Fig. 2. The mixed data chart before applying the clustering technique

Fuzzy C-means clustering technique. Clustering is the most common type of unsupervised learning approach, having a wide range of applications and implementations
in a variety of industries. Clustering is the process of splitting and processing data on

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behalf of an information machine, resulting in a collection of data known as clustering,with each cluster given a unique identification number (ID). For data clustering, theFuzzy C-means (FCM) clustering approach is the most often employed. FCM is a softcluster method in which individually facts argument is given a probability or possibility
score that indicates whether it belongs to that cluster. It accomplishes this by introducing new uncorrelated variables in a sequence that maximizes variance. This logic is
instantly applied to the data matrix, resulting in a membership matrix that shows thedegree of connection between the samples and each cluster.
Taking into consideration the space amongst the centroid and the target value, thismethod assigns participation to each data point corresponding to each centroid. The
data belongs to the cluster more if it is near to the centroid. The actual number for eachdata point should be one [22]. After each cycle, the following formula is used to change
participation and cluster centroid.

 ij  1 /c

 (d /d )
ij

k 1

ik

( 2 / m 1),(1) (ij) x  , j  1, 2, 3,c,vj 
 (ij) 
n

m

i 1
n

i

m

(2)

i 1

where, ‘n’, ‘vj’, ‘m’, ‘c’, ‘µij’, and ‘dij’ denote the number of data points, cluster
center, the fluffiness directory, the number of cluster centers, the connection between
the ith data and the jth cluster center, and the Euclidean distance between the ith data
and the jth cluster center, respectively.
The core impartial of the fuzzy c-means approach is to decrease the Euclidean distance given below
J (U , V ) 

n

c

2

  (ij) x  v .
m

i 1

j 1

i

j

(3)

The Euclidean distance amongst both the ith data point and the jth cluster centeris signified by ‘||xi – vj||’. There are a few mathematical functions that used for FCM
algorithm such as the Euclidean, Manhattan, and Hamming distances. This method isconstructed on categorization. Some of these approaches are as follows:k ( xi  yi)2

Euclidean

(4)

i 1

k

 xi  yi

(5)

 k

 xi  yi  q 1/ q
 i 1
(6)

Manhattani 1Minkowski

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

FCM methodology steps:
Let X = {x1, x2, x3..., xn} represent a set of data points and V = {v1, v2, v3..., vc}
represent a set of centers, then
1. Randomly select ‘c’ cluster centroids
2. O use the following formula to calculate the fuzzy participation ‘µij’:

 ij  1 /c

 (d /d )
k 1

ij

ik

( 2 / m 1)(7)

3. Compute the fuzzy centroids ‘vj’ as follows:
 n
  n

vj  
(  ij ) m xi  / (  ij ) m  , j  1, 2, 3, c
 i 1  i 1






(8)

4. Repeat steps 2 and 3 until the smallest ‘j’ value is attained or ||U(k + 1) – U(k)|| < β
is achieved, whereas the repetition step is signified by the letter ‘k.’, the completion
criteria between [0, 1] is ‘β’, the fuzzy relationship matrix is well-defined as ‘U = (ij)n*c’, and the impartial function is represented by the letter ‘J.’
The student’s performance prediction and clusters dataset is clearly well-defined
as a 17-dimensional dataset, with three features based on student’s performance prediction values and one feature target property cluster number (see Table 3). The FCM
Clustering centroid value briefly defined is given below, whereas Figure 3 shows thethree clusters after the unstructured dataset has been converted to structured data. Thisgraph depicts two clusters: red and green, with centroid values specified by the yellow
star (*). Figure 4 shows FCM determined sum of squared error line chart.
FCM Clustering Centroid Value.
array([[0.49492933, 0.4917658 , 0.51983247,
0.47320961, 0.49158729, 0.52289268, 0.5230012 ,0.47840191, 0.50558121, 0.51672337, 0.49937354,
0.51753685, 0.49081544, 0.48903864, 0.51368392,
0.48831137, 0.51521064],[0.4949292 , 0.49176579, 0.51983274, 0.47320943,
0.49158728, 0.52289266, 0.52300126, 0.47840171,
0.50558125, 0.51672342, 0.49937341, 0.51753673,
0.49081553, 0.48903883, 0.51368394,
0.48831103, 0.51521079],[0.49492982, 0.49176602, 0.51983238, 0.4732094 ,0.49158729, 0.52289266, 0.5230013 , 0.47840182,
0.50558108, 0.51672348, 0.49937376, 0.51753657,
0.49081558, 0.48903829, 0.513684,
0.48831106, 0.51521069]])

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Table 3. FCM three clusters preprocessed for international
high education students performance dataset
GenderNationality

Place of Birth

Stage ID

Grade ID

0.276542

0.456038

0.416909

0.675114

0.664516

0.595424

0.976172

0.329478

0.635595

0.804943

0.181578

0.429730

0.063652

0.445259

0.907755

0.830483

0.088741

0.159844

0.527569

0.263223

0.210892

0.701391

0.766264

0.172061

0.103681

Section ID

Topic

Semester

Relation

Raised Hands

0.959812

0.820159

0.609305

0.522702

0.782175

0.677397

0.132544

0.169182

0.336317

0.587580

0.918585

0.755372

0.045972

0.292576

0.704262

0.741383

0.871822

0.152801

0.884067

0.992723

0.024694

0.419428

0.949947

0.700166

0.426451
Parent School
Satisfaction
0.667879

VisitedResources

Announcements
View

Discussion

ParentAnswering
Survey0.892479

0.164947

0.646486

0.145420

0.624576

0.543965

0.676087

0.104618

0.115494

0.028200

0.060382

0.371900

0.112741

0.355699

0.117836

0.414137

0.987809

0.355952

0.783681

0.145548

0.839285

0.519597

0.009649
Class

Clusters

0.930874

0.996918

20.212968

0.87144520.400581

0.200401

2

0.098311

0.266751

20.616193

0.617324

0Fig. 3. The FCM three clusters

224

0.763605

Student Absence Days

Fig. 4. The FCM sum of squared
error line chart

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

A concern value is produced based on the degree of divergence from particulardimensions and thereby, the based on student’s performance prediction result is classified as valid or suspect, or predictions.
3.2

Classification

The classification is a supervised learning strategy that determines the category ofobservations using training data. The process of learning from a dataset of observations and then categorising the observations into one of several classes or groupings is
known as classification. In our dataset, we test the following classification methods tosee which one performs better.
Multi-layer perceptron algorithm. A multi-layer perceptron (MLP) is a sophisticated optimization technique. It’s finished up of countless perceptron. The algorithm
consists of three layers: an input layer that receives data, an output layer that takes adecision or guesses about the input, and an endless number of hidden layers that act as
MLP’s true processing arrangement. MLP may approximate any continuous function
with a varied number of hidden layers. This technique effectively estimates each andevery linear method. It classifies collections that are not conditionally independent.
Participants achieve this by creating machine learning and predicting models for challenging facts using a more elastic and complicated infrastructure, and this strategy is
widely used to deal with supervised learning challenges [23]. This method, like theSigmoid, Linear, Cost Linear, and Non-linear Regression, is built around classification,
as given below.
Sigmoid
Linear Logistic Regression
Cost Linear Logistic
Regression

S ( z) 

1(1  e  z )y  e ^ (b0  b1* x) / (1  e ^ (b0  b1* x))(Cost (h ( x), y ))  log (h ( x)), if y  1 and(Cost (h ( x), y ))  log (1  h ( x)), if y  0Nonlinear Logistic Regression

Y  f (X, )  

(9)
(10)
(11)
(12)

The main steps of MLP algorithm are given below.
1. The MLP, like the perceptron, advances facts by calculating the partial derivatives of
the input records and the parameters that appear between the input and hidden layer.
This sampling distribution generates a value in the hidden layer. However, unlike anactivation function, we do not increment this value.
2. The MLP employs activation functions in each of their estimated layers. There arevarious processing mechanisms to evaluate, such as rectified linear units (ReLU),
the sigmoid, and the mechanisms. Any of these methods is used to transfer the measured output to the visible layer.

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3. After the expected outcome at the invisible layer has been generated through theactivation function, extract the partial derivatives with the required values and transmit them to another layer inside the MLP.
4. Repeat the above procedures two – three times till the final output is acquired.
5. The estimates will be used at the output to acquire results for either a feed-forward
technique corresponding to the activation methods chosen for only the MLP (in the
scenario of training data), or a selection obtained from the results (in the situationof testing data).
6. End.

Fig. 5. Confusion matrix of MLP

The MLP labels the old data values and predicts the value of the new data. We
try to make predictions fit the labels during preparation. Figure 5 shows the result ofthat MLP confusion matrix. As of this paper, the confusion matrix has the meaning as[[A B][C D]], whereas• A is the frequency of correctly predicted negative instances,• B is the quantity of inaccurate guesses that a positive occurrence has.
• C is the number of inaccurate predictions that a negative occurrence has, and• D is the frequency of right predictions of a positive case.
We used the matrix of MLP Algorithm for assessing the model accuracy and model
loss concepts. This will increase the approximated prediction approach’s accuracy
while also ensuring that the patterns of student’s performance prediction are fulfilled
on a frequent basis. The achieved model accuracy is 0.9583 and further discussed in
Section 5 of Results.
Logistic regression algorithm. The logistic regression (LR) is a ML approach used
for classification procedures. It is a probability hypothesis-based predictive analytic
technique. A LR model is functionally equivalent to a linear regression model. However,LR employs an additional complicated cost-function, known as the sigmoid-function or226

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

often as the logistic-method, rather than a linear function. The LR hypothesis limits thedifferential equation to values amongst 0 and 1. As an outcome, linear-functions flop tocharacterize it since they might have values more than one or even less than zero, which
is not possible under the LR assumption.
The LR algorithm’s major steps are given below and the detail about the LR and its
mathematical functions can be found in [24]:1. Initialize all parameters (B0, B1, etc.).
2. Compute (predict) dependent variable (hθ(x)).
3. Compute cost function (Cost (hθ(x),y)) or any Logistic Regression function.
4. Compute gradient for the cost function.
5. Update all parameters.
6. Repeat steps 2 to 5.
7. End.
The LR model’s true and predicted labels are shown in confusion matrix in Figure 6.
We apply the LR algorithm on our above dataset and label the old data values. It predicts the value of data – where we tried to make our predictions fit the labels during
preparation with the use of the LR algorithm. Figure 6 shows the result of that matrix.
This will increase the approximated prediction approach’s accuracy while ensuringthat the patterns of students’ performance prediction are fulfilled on a frequent basis.
Furthermore, the achieved model accuracy is 0.9583, identical to MLP, and further
discussed in Section 5 of Results.

Fig. 6. Confusion matrix of LR algorithm

Random forest algorithm. A random forest (RF) is a ML technique that is use
to resolve the classifier issues. It makes use of different classifiers, a sort of difficult
resolving system, that makes use of classifying approaches. That’s the technique of
merging numerous categories to solve complicated issues and enhance the effectiveness of the system. The RF approach, which is built on classification tree predictions,

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decides the effectiveness. It makes assumptions by approximating or calculating theresults of numerous trees. The quality of the output increases even as number of nodes
increases [25–26].
The RF algorithm is based on the following steps:
1. Initiate by randomly picking observations through facts.
2. The program will instead create a tree structure for every instance. The outcomes forevery tree structure will then be produced.
3. Throughout this stage, every generated came as a result would be selected or
decided on.
4. Lastly, select the most preferred forecasting outcome as unique of the most
predicted results.
Moreover, the RF algorithm uses specific mathematical functions. This approach, aswell as Mean Squared Error (MSE), Gini (Coefficient, Index, or Ratio), and Entropy[27], may be used by the RF algorithm such as given below.

Gini Coefficient

N

1

N

Mean Squared Error (MSE) ( xi  yi)C( p )

Gini  1i 1Entropy

Entropy 

i

c

i

(13)

(14)

2

  p *log ( p )
i 1

2

i 1

2

i

(15)

Fig. 7. Confusion matrix of RF algorithm

We apply the RF algorithm on our dataset and label the old data values. It predicts thevalue of data – where we tried to make our predictions fit the labels during preparation

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

with the use of the RF approach. Figure 7 shows the confusion matrix. Moreover, theachieved model accuracy is 0.8833 and further discussed in Section 5 of Results.

4

Experimental setup

Python is a high-level scripting language that’s mostly used for general-purpose
programming and machine learning methods, as well as web development and database management. We have used the Anaconda Navigator –>Jupiter Notebook GUI
framework from the Python tool. All the desired algorithms i.e., FCM, MLP, LR, andRF, have been simulated in Python programming language. The dataset is mostly used
to assess student performance. This dataset has 480 tuples (rows) and 17 dimensional
characteristics (columns). We simulate the programs of different combinations of thealgorithms. The first program simulated FCM and MLP schemes, the second program simulated FCM and LR schemes, and the third program simulated FCM and RF
schemes. The configuration of the computer used is as follows:• Second Generation Intel (R) Core (TM) i5-2520M CPU @ 2.50 GHz.
• RAM 4.00 GB.
• The system is a 64-bit operating system.
• Windows 10 (Home).
• 500 GB hard disk.

5

Results and discussions

ML is a scientific approach in which CPUs learn how to resolve complications without being explicitly programmed. DL is now persuasive the ML competition, owing tobetter-quality processes, computation power, and immense datasets. Nonetheless, conventional ML algorithms hold a prominent place in the sector. This research employs anovel technique designed on the integration of FCM. The performance of the classifier
was then compared against other supervised ML algorithms to choose the best classifier. In this study, ML supervised approaches have been considered which include MLP,
LR, and RF (classification techniques), as well as FCM (clustering technique). Furthermore, we combine the algorithms to get the best accurate combination for predicting
the student’s performance. We have formed the different combinations which include
FCM – MLP, FCM – LR, and FCM – RF, and simulated the programs. The achieved
results are compared with the conventional algorithms such as K-Nearest Neighbor(KNN), Support Vector Machine (SVM), and Deep Neural Network (DNN). Tables 4
and 5 show the acquired accuracy and other performance characteristics. The observed
results are also shown in Figures 8 and 9. It is apparent from the results that the combinations FCM – MLP, and FCM – LR are reached at their maximum outcome in terms ofaccuracy i.e., 95.833%. However, the combination FCM – RF is ranked second having
accuracy of 88.33%.

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

Table 4. Model accuracy for the combination of algorithms
for students performance prediction
Algorithms

Accuracy (%)

Algorithms

Accuracy (%)

K-Nearest Neighbor (KNN) [20]69.00

Support Vector Machine(SVM) [20]75.00

Random Forest (RF) [20]79.00

Deep Neural Network(DNN) [20]84.00

FCM – MLP
Proposed Method.

95.833333

FCM – LR
Proposed Method.

95.833333

FCM – RF
Proposed Method.

88.333333

Table 5. Parameter score for the combination of algorithms
for students performance prediction
S/No.

Parameter Score (%)

FCM – MLP

FCM – LRFCM – RF1

Accuracy

0.95833333

0.95833333

0.88333333

2

Precision

0.95833333

0.95833333

0.88333333

3

Recall

0.95833333

0.95833333

0.88333333

4

Sensitivity

0.99555555

0.97142857

0.78947368

5

Specificity

0.97619047

0.99333333

0.94117647

6

F1-Score

0.95833333

0.95833333

0.88333333

Fig. 8. Model accuracy for various combined algorithms for students’ performance prediction

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

Fig. 9. Parameter score for various combinations of algorithms
for students’ performance prediction

6

Conclusion

The effectiveness of ML algorithms for predicting student performance has been
examined in this study. The considered dataset of high education students in this study
was evaluated using a range of algorithms including the fuzzy C-means (FCM), MultiLayer Perceptron (MLP), Logistic Regression (LR), and Random Forest (RF). Initially,
the dataset was preprocessed and clusters were formed using FCM technique. Later, thepreprocessed data was classified by using a number of classification algorithms such
as MLP, LR, and RF. The FCM technique has been particularly combined with eachclassification algorithm to improve the precision. As a result of combining each classification algorithm with the FCM, the achieved accuracy is 95.833%, 95.833%, and88.333%, for the combinations FCM – MLP, FCM – RF, and FCM – LR, respectively.
It is concluded that the combination of FCM with MLP, and FCM with LR yields themost accurate results.

7

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applications and tasks: a survey of the last 10 years,” Education and Information Technologies (Educ Inf Technol), vol. 23, no. 1, pp. 537–553, 2018. https://doi.org/10.1007/
s10639-017-9616-z

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms[4] P. Chen, Y. Lu, V. W. Zheng, X. Chen, and B. Yang, “KnowEdu: a system to constructknowledge graph for education,” IEEE Access, vol. 6, pp. 31553–31563, 2018. https://doi.
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linear regression-based approach to predict student performance,” in Proceedings of theInternational Conference on Advanced Intelligent Systems for Sustainable Development,
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[8] I. D. Livieris, et al., “Predicting students’ performance using artificial neural networks,”
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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms[20] V. Vijayalakshmi and K. Venkatachalapathy, “Comparison of predicting student‘s performance using machine learning algorithms” I.J. Intelligent Systems and Applications, 12,
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[27] Random-Forest-Algorithm, https://medium.com/capital-one-tech/random-forest-algorithmfor-machine-learning-c4b2c8cc9feb, accessed on 2022-05-30

8

Authors

Mirza Azam Baig pursuing MS degree in Computer Science from Sindh Madressatul Islam University, Karachi, Pakistan. He received BS Software Engineering degreein 2017 from Baqai Medical University, Karachi, Pakistan. From 2019 to the present,
he is working as a Software Engineer at Bahria Enterprise Systems & Technologies Pvt
Ltd, Pakistan. His research interests include in Artificial Intelligence, the Internet ofThings, and Deep Learning.
Dr. Sarmad Ahmed Shaikh was born in Pakistan. He received the Ph.D. degree
in Information Technology from the University of Klagenfurt, Klagenfurt, Austria, in2018. He is currently an Assistant Professor with the Faculty of Information Technology, Sindh Madressatul Islam University (SMIU), Karachi, Pakistan. His researchinterests include RF/microwave engineering, radio localization, antenna arrays designing, and wireless sensor networking (IoT) & artificial intelligence.
Dr. Kamlesh Kumar Khatri was born in Pakistan. He obtained Bachelors Degreein Computer Science from Shah Abdul Latif University, Khairpur, Pakistan, in 2006,
the Master’s Degree in Computer Science from SALU Khairpur, Pakistan, in 2010, and
the PhD Degree in Computer Science & Technology from UESTC China in 2016. He
is currently serving at Sindh Madressatul Islam University, Karachi, Pakistan, as an
Assistant Professor in the Department of Software Engineering. His research interests
include Image Processing, Computer Vision, Machine Learning, Knowledge Discovery
and Data Mining, and Content Based Image Retrieval System.
Muneer Ahmed Shaikh was born in Pakistan. He received the Bachelors Degreein Computer Systems Engineering from Mehran University of Engineering & Technology Jamshoro, Pakistan, in 1994, the M.E. degree in Computer Systems Engineeringfrom NED University, Karachi, Pakistan, in 2010, and the Ph.D. degree (Continued) in
Computer Science from SMI University Karachi Pakistan. He is currently an AssistantiJET ‒ Vol. 18, No. 01, 2023

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Paper—Prediction of Students Performance Level Using Integrated Approach of ML Algorithms

Professor in Computer Science, College Education Department, Government of Sindh,
Karachi, Pakistan. His research interests include RF/microwave engineering, radiosource localization, antenna arrays designing, and wireless sensor networking (IoT) &artificial intelligence.
Muhammad Zohaib Khan obtained MS degree in Computer Science from Sindh
Madressatul Islam University, Karachi, Pakistan, in 2022. His research interests include
Data Science, Artificial Intelligence, Machine Learning, Deep Learning, Medical Science & Internet of Things (IoT).
Mahira Abdul Rauf obtained MS degree in Computer Science from Sindh Madressatul Islam University, Karachi, Pakistan, in 2022. Her research interests include
Machine Learning, Artificial Intelligence, Cloud Computing, the Internet of Things,
and blockchain.
Article submitted 2022-09-14. Resubmitted 2022-11-15. Final acceptance 2022-11-15. Final version
published as submitted by the authors.

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Paper—The Degree of Practicing Creative Thinking Skills by Basic School Teachers in Emirate of…

The Degree of Practicing Creative Thinking Skills
by Basic School Teachers in Emirate of Sharjah
from their Point of View
https://doi.org/10.3991/ijet.v18i01.36003

Manal Mahmoud Al-Shaikh1, Najeh Rajeh Alsalhi1,2,3()College of Humanities and Sciences, Ajman University, Ajman, UAE
2
Nonlinear Dynamics Research Center (NDRC), Ajman University, Ajman, UAE
3
Humanities and Social Sciences Research Center (HSSRC), Ajman University, Ajman, UAE
n.alsalhi@ajman.ac.ae
1

Abstract—This study aimed at finding out the degree of practicing creative
thinking skills by basic school teachers in Emirate of Sharjah, from their pointof view. The descriptive – survey methodology was used in the study. The study
sample consisted of (50) female teachers of the basic stage. A questionnaire wasdeveloped to collect the study data. It consisted of (22) items, distributed on
five domains: (fluency, flexibility, sensitivity to problems, taking risk and enriching detail skills). Validity and reliability of the tool were assured. The findings
showed that the degree of practicing creative thinking skills by basic female
school teachers was high from their point of view. There were no statisticallysignificant differences at (α ≤ 0.05) in the degree of practicing creative thinking
skills by the basic female school teachers in Al – Sharjah Emirate attributed toacademic qualification and experience variables. Among the recommendationsof the study was: Supporting the effectiveness of practicing creative thinking
skills of the basic female school teachers in Emirate of Sharjah.
Keywords—skills, creative thinking, Emirate of Sharjah, basic school teachers

1

Introduction

Thinking is a vital activity that characterizes the human being as a complex mental
activity. It is a characteristic of the individual’s awareness of the events and things
going on around him in his life [1]. Thinking is a mental process that a person practices
during his life in light of his efforts to solve the problems or situations he faces basedon his experiences and motives. In view of the problems and challenges we face inall fields and disciplines, the need has become urgent that requires the use of thinking
processes, especially creative thinking, to meet the challenges and the huge explosionof knowledge, and for more fruitful education to employ the higher mental capacities
to creative thinking in a way that develops ideas, performance and abilities in line withthe requirement so advanced education that develops ambition, confidence and skills,
to the maximum event possible, while proving the ability to employ creative thinking

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through communication, through the free expression of what is going on in the mindof the individual, with the discovery of things from several aspects in a creative way.
As well as the individual’s discovery of himself and others and their understanding andsolving the gaps through his creative thinking that pushes him strongly towards theright and peaceful direction to reach a result. Creativity, and then creative thinking, is
not limited to a specific category of individuals or to a field or specialty. This is because
human beings are creative and distinguished in every society, and teachers who havecreative thinking form the main basis for moving towards progress and prosperity. The
role of the teacher is effective in diagnosing strengths and weaknesses, developing his
style of creative thinking, and raising its level, because he is the person most capable ofmaking change for the better and integrating his work and ideas within the frameworkof creative thinking through the appropriate use of the skills of this type of thinking.

2

Research problem

The use of creative thinking skills is very important in light of the rapid developments and tremendous achievements in order to improve performance and excellencein various areas of life, which will contribute to overcoming many obstacles facing theteacher while carrying out his educational tasks by unleashing thinking and creativity
the best conditions for excellence and creativity. Since the skills of creative thinking
empower teachers to deal and harmonize with all parties of the educational process
at a high level of positivity, the short coming in using them will negatively affect theachievement of the goals of the educational process. Hence the problem of the study
emerged by answering the following question:
What is the degree of practicing creative thinking skills by basic school teachers in
the Emirate of Sharjah from their point of view?
2.1

Research aim and its questions

The aim of the current research is to find out the degree of practicing creative thinking skills by basic school teachers in Al – Sharjah Emirate in the United Arab Emirates(UAE) by answering the following two questions:
RQ1: What is the degree of practicing creative thinking skills by basic school teachers from their point of view?
RQ1: Are there any significant differences at (α ≤ 0.05) in the degree of practicing creative thinking skills by basic school teachers in the Emirate of Sharjah
attributed to experience and academic qualification variables?
2.2

Research importance– It is hoped that the results of this research will encourage the competent authorities
in the Ministry of Education to prepare training programs that work to develop thecreative thinking skills to teachers to reach excellence and success in the educational
process.

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– It is hoped that the results of this research will benefit educators in addressing someteaching problems in a creative manner.
– It is hoped from the results of this research the link between educational administration and educational psychology through the psychological variable represented increative thinking.
– It is hoped that the results of this research provide a theoretical framework on theusefulness and importance of creative thinking skills for teachers, and it will be astarting point for research conducted at other levels of study.
2.3

Delimitations of the research

The current research was limited to basic school teachers with all their specializations in the Emirate of Sharjah in the United Arab Emirates for the current academic
year 2020–2021.
2.4

Definition of terms

The research included a number of terms that were defined conceptually and operationally, as follows:• Thinking: A complex concept consisting of three elements represented in complex
cognitive processes, the highest of which is problem solving, and the least are understanding and application with knowledge of the content of the material, with theavailability of preparedness and various personal factors, specially attitudes and tendencies [2, 3].
• Creative thinking: is a purposeful activity that results in new innovative ideas thatare not familiar with theoretical or applied situations in one of the educational or life
fields so that these solutions and products are characterized by modernity, novelty,
and complexity [4–6].
• The operational definition of creative thinking skills: The ability of the teacher andthe extent of his flexibility to interact with all those in charge of the educational
process in its creative nature, which can be measured through the answers of thesubjects of the research sample to the items of the questionnaire developed by the
researcher and used in this research.

3

Theoretical literature

3.1

Creative thinking concept

Creativity (language) means the one who invents a thing: he created it, and foundit [7]. Guilford defined creative thinking as it was referred to in [8] as the ability tofind multiple and diverse solutions, i.e. the ability to develop ideas. While [9, 10]
had defined creative thinking as producing a new and appropriate answer, product, or
solution to an open – ended problem. As for [11], he defined creative thinking as theiJET ‒ Vol. 18, No. 01, 2023

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ability to imagine with creative participation, both physically and mentally, in various
activities in an unusually successful manner while solving problems with the use oftools from skilled people to develop new methods and ideas.
3.2

Levels of creative thinking

Taylor classified creative thinking into five levels, according to [12] mentioned, as
follows:
1. Expressive creativity: Refers to free and independent expression to develop an idea
regardless of its quality and without the need for skills or originality.
2. Productive creativity: refers to the product of the growth of the expressive level tolead to the production of complete works characterized by the restriction and controlof free activity.
3. Inventive creativity: Refers to invention and discovery involving flexibility in perceiving new relationships between previously separate parts.
4. Innovative creativity: Requires a strong and high ability to abstract visualization ofthings to be improved by the creator.
5. Emergent creativity: It is the highest level of creativity that includes the conceptionof a new principle or access to a theory or law.
3.3

Factors affecting creative thinking

The main factors affecting creative thinking according to [13, 14] are as follows:
1. Intelligence: A condition for creativity in a certain percentage, but it is not sufficient
to develop creativity.
2. Flexibility in thinking: It refers to the ability to move from one idea to another andlook from multiple angles and directions.
3. Convergent and divergent thinking: It is closely related to the stored information andthe form in which it is, with an awareness of the relationships between them.
4. Motivation: It is expressed by the integration of internal and external motives, whichis consisted a stimulus for creative thinking that stems from the congruence of socialand personal needs.
5. The family: Is the first influencer in shaping the child’s personality elements andlearning methods, through which the child learns to be traditional or creative, independent of his patterns of thinking and behavior.
6. The school: The school plays a major role in nurturing and developing creativity,
whether by paying attention to depositing and finding appropriate educational programs or by providing incentives for creative students to motivate them to complete
to achieve more creativity.

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3.4

The importance of creative thinking

In [15, 16], they pointed out and explained the importance of creative thinking withthe following points:
1. Develops the individual’s ability to elicit new ideas and develops sensitivity to the
problems of others.
2. Contributes to realization of the creative self, and the development of creative products with the development of talents and a better understanding of the world.
3. It leads to openness to new ideas, responding effectively to opportunities, challenges,
responsibilities, managing risks, and adapting to changes.
4. Stimulates the tendency to cooperate with others to discover ideas.
5. Contributes to motivate schools to be a suitable environment for discovering talents
and working to develop them through specialized programs.
6. Contributes to the development of learning methods and patterns to become more
effective.
7. Contributes to the development of the individual’s ability to deal with challenges andlife situations in a more creative way.
8. It helps the individual to reach a successful solution to the problem in a creative way.
Advantages of creative thinking
Creative thinking, as [11] pointed out, has the following advantages:
1. The presence of high flexibility in the ideas that are presented.
2. There is a typical way of human thinking.
3. Find new ways to solve different problems.
4. Researching the details and getting into the depth of the problems to come up withdrastic solutions.
5. Rapid response to emergency problems and to find quick solutions.
3.5

Creative thinking skills

Creative thinking skills are determined as follows:
1. Fluency skill: It means the speed or ease in issuing ideas or solutions to problems
in accordance with the requirements of the real environment, so that the idea areorganized, accurate and issued by knowledge. Fluency is measured by the ability to
produce the largest possible number of solutions or ideas in a specific time. Fluency
also represents the quantitative aspect of creativity [17, 18].
2. Flexibility skill: It means the ability to change the direction of thinking and generatevarious ideas to solve a problem or change a viewpoint towards the problem being
treated and look at it from different angles.
3. Problem sensitivity skill: It means the ability to perceive weaknesses or shortcomings in a situation or something, which the average individual does not notice inmost cases [19, 20].

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4. The skill of enriching the details: It means the ability to add new and diverse details
to an idea or to solve a problem that would help develop and enrich it [21, 22].
5. The skill of accepting risk: is intended to take the initiative in adopting new ideas andmethods, and searching for solutions to them at the same time, when the response iscapable of bearing the risk resulting from the work he is doing, and is ready to face
the resulting responsibility [23, 24].
3.6

Stage of creative thinking

The creative thinking process consists of six stages [25]:
1. The stage of inspiration is the stage of generating large numbers of ideas that arecharacterized by being uninhibited, and characterized by spontaneity, experience,
intuition and risk taking.
2. Illustration stage is a stage that focuses on the goals by asking the following mainquestions:– What am I trying to achieve here?
– What am I trying to say?
– What is the exact problem I am trying to solve?
– What would I like the work done to look like?
– How can I invest the ideas I have in my hands?
– Where does this idea lead me and what can I make of it?
3. Extraction stage: is the stage of carefully examining the generated ideas and determining what is suitable for them to work, where the ideas are scrutinized from theinspiration stage, and in light of the results from the illustration stage, the best ideas
are selected for further development or integrated into better ideas.
4. Nomination stage: This stage is carried out with sincere determination for the best
ideas that have been selected. Here the real work is done with seriousness and persistent effort towards achieving the goal.
5. Evaluation stage: In this stage, the work is examined and previewed in search ofstrengths and weaknesses in order to invest and reinforce the strengths. This stage isrequired to return to the nomination stage to respond positively to the proposals formodification and improvement.
6. The stage of developing ideas: It is the stage of devotion to other work and leaving
the current work, and thinking about it from time to time without losing sight of the
mind.
3.7

Obstacles to creative thinking

Some obstacles that prevent the development of creative thinking:– Family obstacles: They are the most important influences that affect the child’s personality, including the low standard of living of the family, and the educational levelof the parents, and their neglect of the interests and desires of their children [26].

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– Personal obstacles: These obstacles are represented in personality weakness, lack ofself – confidence, compliance with others, and lack of awareness of environmental
stimuli.
– Emotional obstacles: that conflict with the freedom to explore and process ideas.
Despite our ability to use concepts and ideas flexibility and fluently, these obstacles
prevent us from conveying ideas to others in a manner that meets acceptance andsatisfaction [27].
– School obstacles that include the teacher, his culture, teaching methods, attitudes
and ideas toward the teaching profession, his relationship with students, the effectiveness of school administration, its policies and leadership styles, and its role inencouraging students to acquire creative thinking skills, and discouraging students
to think and use their diverse mental abilities [19].

4Related work

In [28], they conducted a study aimed at examining the effectiveness of a training program based on the self – questioning strategy in developing creative thinking skills (fluency, flexibility and originality) among tenth grade students in Azraq camp for Syrian
refugees in Jordan. The study sample consisted of (40) female students distributed into
two groups: experimental and control. The Torrance scale of creative thinking was used.
The results of the study indicated that there were statistically significant differences
between the performance means of the experimental and control groups, in favor of theexperimental group. Also, [29] conducted a study aimed at investigating the effect of age
and gender variables on creative abilities of seventh, eighth and ninth grades of the Sahab
pioneering center. The study sample consisted of (60) male and female students. The
Torrance test for verbal and formal creative thinking was applied. The results showedthat there were no statistically significant differences according to the gender variable at(α ≤ 0.05) on the dimensions of fluency, flexibility and originality. There were also no
statistically significant differences at (α ≤ 0.05) due to the age variable on the students’achievement in the verbal and formal tests. Moreover, [30] conducted a study aimed atdeveloping an educational unit from the eighth grade geography book in light of the habits of mind and measuring its effect on the creative thinking of female students in Jordan.
The sample of the study consisted of (83) female students distributed into two groups:
experimental and control. The findings of the study indicated that there were statisticallysignificant differences between the performance means of the experimental and control
groups, in favor of the experimental group. The results also indicated a weakness in theactivities related to creative thinking skills. Likewise, the aim of the study of [31] wasto search for the creative thinking skills of the tenth grade students in mathematics. The
sample of the study consisted of (35) students. The quantitative and qualitative methodologies were used in this study. The findings showed that the students had different skills
in mathematics and creative thinking. Furthermore, Adam and Mujib (2020) carried out
a study aimed at improving students’ critical and creative thinking skills, through a multivariate analysis of the experiments and gender model. The study sample consisted of(328) participants from five different universities in Indonesia. The multi – skills lab

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activity model and the high – level thinking lab were used, with gender as an influencing
factor in the successful learning process. A quasi – experimental methodology was used.
The result showed that the experience affected the learning outcomes more than thegender variable. The results indicated that the multi – skills lab activity model improves
students’ critical and creative thinking skills better than the high – level thinking lab.
Also, [32, 33] conducted a study aimed at determining the effect of the generative learning model for the skills of solving mathematical problems, and the skills of mathematicalcreative thinking of fifth grade students. The study sample consisted of (75) students.
Data were collected using essay tests to solve mathematical problems and the skillsof mathematical creative thinking. Data were analyzed using the natural state test andharmonization test as well as assumptions using SPSS. The results showed that there
was an effect of the generative learning model on mathematical problems. While [34]carried out a study aimed at testing the effectiveness of learning methods on creative
thinking skills and finding out the interaction between multimedia and learning styles in
influencing creative thinking skills. The total sample consisted of (74) male and female
students in the tenth grade, who were divided into two groups, experimental and control.
The quasi – experimental methodology was used. The data on creative thinking skills
were tested using (N – Gain). The learning methods were quantitatively analyzed to findout the effectiveness of the research variables. One-way Analysis of Variance (ANOVA)
was used in manipulating data. The results showed that interactive multimedia had aneffect on students’ creative thinking skills. The learning styles and interactions of the two
independent variables did not affect the creative thinking skills of the students.

5

Methodology

The survey descriptive methodology was used as the appropriate methodology forthis research. The questionnaire was used as a means of data collection.
5.1

Research population

The research population consisted of all female basic school teachers working inprivate schools in Emirate of Sharjah, for the current academic year (2020 / 2021).
5.2

Research sample

A random sample of basic female school teachers in the Emirate of Sharjah wasselected using the simple random sampling method. Their number reached (50) female
teachers, representing the basic female school teachers in the Emirate of Sharjah.
5.3

Research tool (Creative thinking skills questionnaire)

The tool of the research was developed by reviewing the theoretical literature related
to the subject of the research, and related previous studies such as: [35, 36]. The questionnaire, in its initial form, consisted of (22) items distributed into five domains.

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5.4

Validity of the research tool

The validity of the research tool was confirmed using face validity, by presenting the
research tool in its initial form, to a number of arbitrators in educational, psychologicaland administrative sciences, from professors working in UAE universities. They wereasked to express their opinion regarding the items of the questionnaire in terms of clarity and accuracy of its formulation, and whether the items need to be modified and the
proposed amendment. An approval rate of 80% or more of the arbitrators was for theitem for adoption in the questionnaire. The arbitrators’ notes regarding the linguistic
formulation of some items were also taken into consideration.
5.5

Reliability of the research tool

To verify the reliability of the research tool, Cronbach Alpha equation was used
to find out the internal consistency of the tool, as well as using the half split method.
They were applied to a pilot sample from the research population and from outside theresearch sample. It consisted of (10) female school teachers. The reliability coefficientvalues, using Cronbach – Alpha equation ranged between (0.90–0.97). As for the reliability coefficients by the split – half method, they ranged between (0.76–0.95). These
values are acceptable in this type of research. Table 1 shows that.
Table 1. The reliability coefficients of the research tool using Cronbach Alphaequation and the split half method
No.

SkillsReliability Coefficient Using
Cronbach – Alpha

Reliability Coefficient Using
Split – Half Method

1

Fluency

0.91

0.72

2

Flexibility

0.90

0.83

3

Sensitivity to problems

0.97

0.93

4

Enrichment with details

0.91

0.95

5

Accept the risk

0.95

0.94

Total

–

0.95

6

Results and their discussion

6.1

Firsts: Results related to the answer to the first question: What is thedegree of practicing creative thinking skills by basic school teachers
from their point of view?

To answer this question, means and standard deviations were calculated. The rank
and degree of practice were determined, for the domains of practice of the teachers of
the basic stage of creative thinking skills in the Emirate of Sharjah from their point ofview. Table 2 shows that.

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Table 2. Means, standard deviations, ranks and the degree of practicing
creative thinking skills by basic stage teachers in the Emirate of Sharjah
from their point of view in descending order
No.

SkillsMeanStandard Deviation

Rank

Degree of Practice4

Enrichment with details

3.93

0.72

1

High

2

Flexibility

3.88

0.63

2

High

3

Sensitivity to problems

3.82

0.70

3

High

1

Fluency

3.75

0.60

4

High

5

Accept the risk

3.61

0.90

5

MediumTotal score

3.79

0.61

–

High

It is noticed from Table 2 that the mean of the degree to which basic school teachers
in the Emirate of Sharjah practice creative thinking skills from their point of view was
high. Its mean was (3.79) and a standard deviation of (0.61). The skill of enrichment
in details ranked first with a mean of (3.93) and a standard deviation of (0.72) and ahigh degree. In the last rank came “Accept the risk” skill, with a mean of (3.61) anda standard deviation of (0.90) and a medium degree. This result may be attributed to thefact that teachers have adapted to technological developments, communication technology, diversity in methods of dealing with others, presentation of the study material andpractice of creative thinking to a high degree. As for the items of each skill, the results
were as follows:
1. Enrichment with details skill
Means, standard deviations, determination of ranks and the degree of practicing“Enrichment with details” items by basic stage teachers in the Emirate of Sharjah fromtheir point of view were found. Table 3 shows that.
Table 3. Means, standard deviations, ranks and the degree of practicing“Enrichment with details” items by basic stage teachers in the Emirate of Sharjah
from their point of view in descending order
No.

Items

Mean

Standard
Deviation

Rank

Degree ofPractice

18

Expand and enrich the main and secondary ideas

3.96

0.77

1

High

19

Presenting multiple evidence to support the opinion

3.84

0.82

2

High

3.93

0.72

–

High

Total score

Table 3 shows that the degree of practicing the skill of “Enriching with details” bybasic school teachers in the Emirate of Sharjah, from their point of view was high. The
mean was (3.93) with a standard deviation of (0.72). The two items of this domain cameto a high degree. Item (18) came in the first rank which states that “Expand and enrich
the main and secondary ideas”. Its mean was (3.96) and a standard deviation of (0.77).
While item (19) which stated “Presenting multiple evidence to support the opinion”
came in the second and last rank. Its mean was (3.84) and a standard deviation of (0.82).

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This high result may be attributed to the fact that basic school teachers pay attention
to the details. They clarify and explain these details to student, as well providing relevant knowledge and information to facilitate the students’ understanding of the details
related to the subject matter. This result may be due to teachers seeking to expand theirstudents’ perceptions with information that develops their creativity through their useof various teaching methods.
2. Flexibility skill
Means, standard deviations, determination of ranks and the degree of practicing“flexibility” items by basic school teachers in Emirate of Sharjah from their point ofview were found, as shown in Table 4.
Table 4. Means, standard deviations, ranks and the degree of practicing“flexibility” items by basic school teachers in the Emirate of Sharjah
from their point of view in descending order
Items

Mean

Standard
Deviation

Rank

Degree ofPractice

11

The ability to see things from different angles

4.02

0.87

1

High

13

Use a variety of methods to express an idea

4.00

0.97

2

High

9

Flexibility to change a situation when he isconvinced that it is incorrect

3.91

0.87

3

High

10

Flexibility in work performance

3.80

0.87

4

High

12

Respecting the views of teachers

3.67

0.80

5

Medium

3.88

0.63

–

High

No.

Total score

Table 4 showed that the degree of practicing the skill of “flexibility” by basic school
teachers in the Emirate of Sharjah from their point of view was high. Its mean was(3.88) and a standard deviation of (0.63). The means ranged between (4.02–3.67). In thefirst rank came item (11) which states “the ability to see things from different angles”.
Its mean was (4.02) with a standard deviation of (0.87), and at a high degree. While
item (12) that states “Respecting the views of teachers” came in the last rank with amean (3.67) and a standard deviation of (0.80). This result may be attributed to the factthat teachers have the ability to look at the situations they face during their school day
from different angles to form a clear vision that enables them to make a rational decision about those situations. This result may be due to the fact that teachers use different
methods when they want to express ideas related to the topic of the lesson to facilitate
students’ awareness of those ideas of concepts. This result may be attributed to teachersrespecting the views of other teachers, when they present ideas and views that seemdifferent, which may lead to the presentation of many ideas, some of which may be
useful in developing the educational process.
3. Sensitivity to problems
Means, standard deviations, determination of ranks and degree of practicing theitems of “sensitivity to problems” skill by basic school teachers in the Emirate of
Sharjah from their point of view were calculated. Table 5 shows that.

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Table 5. Means, standard deviations, ranks and the degree of practicing“sensitivity to problems” by basic school teachers in al – Sharjah Emirate
from their point of view in descending order
No.

Items

Mean

Standard
Deviation

Rank

Degree ofPractice

16

Detailed insight into problems

3.88

0.63

1

High

17

Demonstrate enthusiasm in dealing with problems

3.84

0.65

2

High

14

Have patience to solve complex problems

3.80

0.69

3

High

15

Knowing the shortcomings of the work

3.77

0.80

4

High

3.82

0.70

–

High

Total score

Table 5 shows that the degree of practicing the “sensitivity to problems” domain bybasic school teachers in the Emirate of Sharjah from their point of view was high. The
mean was (3.82) with a standard deviation of (0.70). All the items of this skill came in
the high degree. The means ranged between (3.88 – 3.77). Item (16) came in the first
rank, which states “Detailed insight into problems”. Its mean was (3.88) and a standard
deviation of (0.63). Item (15) that states “Knowing the shortcomings of the work” camein the last rank. Its mean was (3.77) and a standard deviation of (0.80). This result may
be attributed to the fact that teachers have a high sense and a comprehensive awarenessof the problems they face when performing their educational work. They also havean accurate perception of the problems and in detail to take note of them and work toaddress them as soon as possible. This high result may be due to the teachers having
enough enthusiasm that motivates them to deal with problems in a positive way that
leads to solve or overcome them. As well as, the teachers being patient and knowing the
weaknesses related to the work they practice.
4. Fluency
Means and standard deviations were calculated. Ranks and degrees were determinedfor practicing the fluency skill items by basic stage teachers in the Emirate of Sharjah,from their point of view. Table 6 shows that.
Table 6. Means, standard deviations, ranks and the degree of practicing“Fluency” skill by basic school teachers in the Emirate of Sharjah fromtheir point of view in descending order
No.

Items

Mean

Standard
Deviation

Rank

Degree ofPractice3

Excellence in discussion with high skill, to convinceothers of new ideas

3.91

0.67

1

High

8

Performs his job in a sophisticated and developed style

3.89

0.71

2

High

7

Constantly looking for new ideas

3.80

0.84

3

High

5

Talk about some topics broadly and with great
confidence

3.78

0.90

4

High

6

Monitoring opportunities to develop them, in order toinvest them in work

3.76

0.80

5

High
(Continued)

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Table 6. Means, standard deviations, ranks and the degree of practicing“Fluency” skill by basic school teachers in the Emirate of Sharjah fromtheir point of view in descending order (Continued)
No.

Items

Mean

Standard
Deviation

Rank

Degree ofPractice2

Encouraging the spirit of teamwork to develop
students’ style

3.71

0.76

6

High

1

Providing teachers with new ideas to improve theirperformance

3.67

0.83

7

Medium4

Accepts constructive criticism to develop his style

3.64

0.86

8

MediumTotal score

3.75

0.60

–

High

It is noted from Table 6 that the degree of practicing the items of “Fluency” skill
by basic stage teachers in Al – Sharjah Emirate from their point of view was high. Its
mean was (3.75) and a standard deviation of (0.60). The items of this domain came in
the high and medium degrees. The means ranged between (3.91–3.64). Item (3) whichstates “Excellence in discussion with high skill to convince others of new ideas”, camein the first rank. Its mean was (3.91) and a standard deviation of (0.67) with a highdegree. While item (4) that states “Accepts constructive criticism to develop his style”
came in the last rank. The mean was (3.64) and a standard deviation of (0.86) with amedium degree. This high result may be attributed to the fact that basic stage teachers
in the Emirate of Sharjah are highly skilled in terms of expression and discussion withothers to convince them of the new ideas they present, and they have verbal fluency thatenables them to employ their communication with other in an effective manner. This
helps them achieve their goals effectively, because they possess accurate knowledge.
This result may be due to teachers’ acceptance of constructive criticism offered byothers, in order to develop their own style of dealing and expressing the subjects they
are assigned to teach.
5. Accept the risk skill
Means and standard deviation were calculated. Ranks and degrees were determinedfor practicing “accept the risk” skill items by basic stage teachers in Al – Sharjah
Emirate, from their point of view. Table 7 illustrates that.
Table 7. Means, standard deviations, ranks and the degree of practicing“accept the risk” skill by basic stage teachers in Al – Sharjah Emirate
from their point of view in descending order
No.

Items

Mean

Standard
Deviation

Rank

Degree ofPractice

20

The ability to adjust his style to meet an emergency
situation

3.85

0.61

1

High

22

Adopting ideas and alternatives to confront the
problem

3.60

0.94

2

High

21

Adopting the creative ideas presented by teachers

3.42

0.92

3

MediumTotal score

3.61

0.90

–

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Table 7 shows that the degree of practicing the items of “accept the risk” skill bybasic stage teachers in the Emirate of Sharjah from their point of view was medium.
The mean was (3.61) and a standard deviation of (0.90). The items of this domain
ranged between (3.85 – 3.42). Item (20) that states “The ability to adjust his style tomeet an emergency situation” came in the first rank. Its mean was (3.85) and a standard
deviation of (0.61) with a high degree. While item (21) which states “Adopting thecreative ideas presented by teachers” came in the last rank. The mean was (3.42) and astandard deviation of (0.92), with a medium degree. This result may be due to the factthat basic stage teachers in the Emirate of Sharjah have the ability to adapt their styleto the nature of the situation they are facing, especially the emergency situation, which
is usually surprising and requires a quick and appropriate solution. It also needs deep
thinking about that situation, and familiarity with all its aspects, in order to be able
property and rationally handle the situation that the teacher is going through, when
performing the tasks assigned to him. This result may be attributed to the teacher’s
adoption of creative ideas, suggestions and alternatives that teachers put forward to
face some problems during the school day. This may help the teacher and the school
administration to overcome many of the daily problems within the school.
Second: Are there any significant differences at (α ≤ 0.05) in the degree of practicing creative thinking skills by basic school teachers in the Emirate of Sharjah
attributed to experience and academic qualification variables?
This question was answered in light of its two variables (experience and academic
qualification), as follows:
1. Experience variable
Means and standard deviations of creative thinking skills were calculated according
to the variable of experience. Table 8 shows that.
Table 8. Means and standard deviations of the degree of practicing creative
thinking skills by basic stage teachers in the Emirate of Sharjah
according to the experience variable
No.
1

2

3

SkillsFluency

Flexibility

Sensitivity to problems

Level of Experience
Less than 5 years

SampleSize

MeanStandard
Deviation

25

3.88

0.81

From 5 – less than 10 years

10

3.90

0.60

10 years and above

15

3.85

0.61

Total

50

3.88

0.63

Less than 5 years

25

3.85

0.60

From 5 – less than 10 years

10

3.73

0.52

10 years and above

15

3.68

0.68

Total

50

3.78

0.60

Less than 5 years

25

3.86

0.55

From 5 – less than 10 years

10

3.98

0.74

10 years and above

15

3.67

0.88

Total

50

3.82

0.69
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Paper—The Degree of Practicing Creative Thinking Skills by Basic School Teachers in Emirate of…

Table 8. Means and standard deviations of the degree of practicing creative
thinking skills by basic stage teachers in the Emirate of Sharjah
according to the experience variable (Continued)
No.

Skills4

Enrichment with details

5

Level of Experience
Less than 5 years

Accept the risk

Total score

SampleSize

MeanStandard
Deviation

25

3.82

0.71

From 5 – less than 10 years

10

3.95

0.71

10 years and above

15

3.84

0.65

Total

50

3.85

0.68

Less than 5 years

25

3.70

0.74

From 5 – less than 10 years

10

3.45

1.50

10 years and above

15

3.23

1.31

Total

50

3.51

0.93

Less than 5 years

25

3.88

0.57

From 5 – less than 10 years

10

3.88

0.66

10 years and above

15

3.71

0.68

Total

50

3.79

0.61

It is noticed from Table 8 that there were apparent differences between the values ofthe means to the degree of practicing creative thinking skills by basic stage teachers in
the Emirate of Sharjah, from their point of view, according to the variable of teacher
experience. Teachers of the two categories of experience (Less than five years, andfrom five to less than ten years) obtained the highest mean of (3.88). To determine if thedifferences were statistically significant at (α ≤ 0.05), One – way analysis of variance(ANOVA) was applied. The results were as shown in Table 9.
Table 9. One – way ANOVA to find out the significance of the differences in the
degree of practicing creative thinking skills by basic stage teachers in the Emirate of
Sharjah according to the teachers’ experience variable
Source of Variation

Fluency skill

Flexibility skill

Sensitivity to problemsskill

Sum ofSquares

Df

Mean
Squares

Between groups

0.281

2

0.141

Within groups

5.633

47

0.372

Total

5.914

49

Between groups

0.026

2

0.013

Within groups

7.317

47

0.412

Total

7.347

49

Between groups

0.10102

0.051

Within groups

0.0692

47

0.478

Total

0.170249

F – Value

Level of
Significance

0.378

0.688

0.031

0.969

0.106

0.900
(Continued)

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Table 9. One – way ANOVA to find out the significance of the differences in the
degree of practicing creative thinking skills by basic stage teachers in the Emirate of
Sharjah according to the teachers’ experience variable (Continued)
Source of Variation
Enrichment with details
skill

Accept the risk

Between groups

Sum ofSquares

Df

Mean
Squares

0.560

2

0.280
0.489

Within groups

0.541247

Total

0.101249

Between groups

1.930

2

0.965

Within groups

16.278

47

0.559

Total
Total score

Level of
Significance

0.573

0.568

1.726

0.182

0.184

0.833

49

Between groups

0.142

2

0.071

Within groups

6.226

47

0.386

Total

F – Value49

The results in Table 9 showed that there were no statistically significant differencesat (α ≤ 0.05) in the degree to which the basic stage teachers in the Emirate of Sharjah
practice creative thinking skills according to the variable of teacher experience. The
F – value for the total score was (0.184) at (0.833) level of significance. It is inferredfrom this result that the teacher’s experience variable does not affect the responses ofteachers in the Emirate of Sharjah, when they practice creative thinking skills. This
result may indicate the agreement of these teachers to describe their practice of creative
thinking skills, regardless of the length of their experience, whether long or short. It
seems that these five skills are clear to teachers, who are fully aware of them despite
their different experiences.
2. Academic qualification variable
Means and standard deviation were calculated to the degree of practicing creative
thinking skills by basic stage teachers in the Emirate of Sharjah. The (t-test) for two
independent samples was also used to find out the significance of the differences among
teachers according to their academic qualification. Table 10 clarifies that.

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Table 10. Means, and standard deviations of the degree of practicing creative
thinking skills by basic stage teachers in Al – Sharjah Emirate and t-test for
two independent sample according to teachers’ academic qualification
No
1
2
3
4
5

SkillsFluency
Flexibility
Sensitivity to problems
Enrichment withdetails
Accept the risk
Total score

Levels of Academic
Qualification

Sample
Size

Mean

S.D.

Bachelor

43

3.84

0.61

Master

07

3.54

0.53

Bachelor

43

3.94

0.57

Master

07

3.64

0.80

Bachelor

43

3.92

0.64

Master

07

3.56

0.77

Bachelor

43

3.85

0.70

Master

07

3.69

0.68

Bachelor

43

3.53

0.93

Master

07

3.42

0.99

Bachelor

43

3.85

0.59

Master

07

3.57

0.68

T-value

Level of
Significance

0.341

0.186

0.301

0.20

0.441

0.157

0.64

0.525

0.30

0.765

0.241

0.221

The results in Table 10 showed that there were no statistically significant differencesat (α ≤ 0.05) in the degree of practicing creative thinking skills by basic stage teachers
in the Emirate of Sharjah, according to teachers’ academic qualification, for the total
score and all domains of skills. The t – value for the total score was (0.241) at (0.221)
level of significance. This result may be attributed to the fact that the educational qualification variable for teachers is not influential in making a statistically significant difference according to the level of qualification. This may indicate that teachers agree indescribing their use of creative thinking skills despite their different academic qualifications (bachelor or master).

7

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8

Authors

Manal Mahmoud Al-Shaikh, College of Humanities and Sciences, Ajman University, Ajman, UAE.
Najeh Rajeh Alsalhi, College of Humanities and Sciences, Ajman University,
Ajman, UAE; Nonlinear Dynamics Research Center (NDRC), Ajman University,
Ajman, UAE; Humanities and Social Sciences Research Center (HSSRC), Ajman
University, Ajman, UAE.
Article submitted 2022-09-12. Resubmitted 2022-10-17. Final acceptance 2022-10-19. Final version
published as submitted by the authors.

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

An Analysis of the Categories and Structuresof Expertise for Students’ Cognitive States
https://doi.org/10.3991/ijet.v18i01.37123

Mingzhe Niu(*)

College of Innovation and Entrepreneurship, Zibo Vocational College, Zibo, China
11017@zbvc.edu.cn

Abstract—Knowledge networks play an important role in the process ofknowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge.
Existing research lacks insight into the internal structural features of knowledge
networks constructed from expertise. There is also a lack of effective methods
for constructing personalised knowledge networks for students’ cognitive states.
This paper analyses the categories and structures of expertise for students’ cognitive states, and presents in detail a grey prediction algorithm to identify students’cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students’ cognitive states, and analyses
the knowledge network structure from the perspectives of paths and statisticalproperties. After that, the paper gives a method for analysing the knowledge flowof the expertise network. The experimental results validate the effectiveness ofthe proposed method.
Keywords—cognitive states, categories of expertise, knowledge network
structure analysis

1

Introduction

With the development and popularity of the Internet, online learning methods thatmeet personalised learning needs for students are increasingly accepted and recognised[1–8]. However, there are differences in the needs of students with different cognitive
levels for learning resources [9–12]. To prevent students from becoming “knowledgelost” when faced with a huge load of learning resources, it is necessary to predict
students’ cognitive levels and correlate them with the appropriate expertise for theirlearning [13–15]. Knowledge networks play an important role in how students acquireand share knowledge. Connecting students to knowledge requires an analysis of its
complex structural features [16–21].
In terms of student cognitive level assessment, Hou [22] introduced the conceptof Teaching for Ability (TFA) based on traditional Internet teaching, using big data
to calculate students’ cognitive abilities and using the assessment results of students’cognitive abilities to drive a tailored Internet learning programme for each student
on a case-by-case basis. Educational programmes for students with low cognitive254

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

ability were identified in the data analysis. In terms of knowledge association, Xu andJiang [23] proposed a personalised recommendation algorithm for online educational
resources based on knowledge association. Firstly, online educational resources werecollected based on association rules. Secondly, the firefly algorithm was used to classify the online educational resources. Then, a vector space function was constructed tofilter the classified online education resources. Online learning platforms are prone to
information overload, as they contain a large number of diverse resources. To address
this problem, Jia et al. [24] explored collaborative filtering recommendation (CFR) for
online learning resources based on a knowledge association model. Knowledge units
were extracted from the semantic information of online learning resources (OLRs) tobuild a knowledge association model for OLR recommendations. A CFR algorithm
was designed to combine semantic adjacency with learning interests, and was used toquantify the semantic similarity of OLRs. In terms of knowledge structure analysis,
Prasetya et al. [25] aimed to investigate the impact of extended scratch-build (ESB)
concept mappings on student learning outcomes, including comprehension, mapping
size and quality of knowledge structure. ESB is an extended open-ended technique that
requires students to link pre-existing original concept maps to new additional maps onrelated material topics. ESB extends concept mappings by adding new propositions
and linking them to previously existing mappings. In this way, it extends the concept
mapping to enhance meaningful learning.
The existing research results can serve as reference for further in-depth research.
However, there are still some problems to be solved, such as the lack of in-depth
exploration for the internal structural characteristics of the organisation of knowledge
networks constructed by expertise. There is also a lack of effective methods for constructing personalised knowledge networks oriented to students’ cognitive states. This
provides some opportunities for the research in this paper. In response, this article conducts an analysis of the categories and structures of expertise for students’ cognitive
states. Chapter 2 describes in detail the grey prediction algorithm for student cognitive state identification. Chapter 3 describes the categories of knowledge nodes within
the expertise network for students’ cognitive states, and then analyses the structureof the knowledge network from the perspectives of paths and statistical properties.
The paper finally presents a method for analysing the knowledge flow of the expertise
network. The experimental results validate the effectiveness of the proposed method.

2

Grey prediction of students’ cognitive state

Figure 1 provides statistics on the influential elements of expertise cognition, withthe core elements including the object of study, functional values, knowledge architecture and cognitive paths. As shown in the figure, the connotations of students’ expertise
cognition are interpreted as classifying, connecting, reinforcing and innovating expertise, i.e., selecting the cognitive paths that match their cognitive state to carry out therelevant research, while interpreting and analysing the categories of expertise to construct their knowledge network architecture.
This paper summarised the cognitive state of students’ participation in the online
learning process as the process shown in Figure 2. On the basis of known expertise,iJET ‒ Vol. 18, No. 01, 2023

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

the network is associated with unknown expertise that meets the students’ cognitive
states. Then, it is internalised to reconstruct the expertise network, which is constantly optimised, modified while providing students with expertise that meets theircurrent cognitive state as they continue to learn. Accordingly, the network is gradually
rationalised and more science-based.

Fig. 1. Influential elements of expertise cognition

Fig. 2. Schematic diagram of the principle of cognitive state enhancement

This paper uses a grey prediction method to identify the cognitive states of students
participating in online learning. The method further obtains the patterns of change instudents’ cognitive states by at first identifying the degree of development trends ofthe various factors influencing students’ cognitive states and then generating the original influencing factor data for processing. Specifically, based on the obtained regularsequence of influencing factor data, the paper constructed a differential equation model
for predicting the future evolution trend of students’ cognitive states, which takes theform of a single-series first order linear differential equation model GM(1,1).

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Assuming that the non-negative canonical parameter is represented by µ and thepenalty term is represented by pt     , the traditional Lasso parameter estij 1

j

j

j 1

mate is defined through the following equation.
γˆ (lasso) = argmin b −
2

γ

t

∑
j =1

2

a jγ j + µ

t

∑γ
j =1

(1)

j

To make the coefficients somewhat adaptive, weights are assigned to the different
1 ˆ1coefficients in this paper. Assuming weights ωˆ j =θj=, (α>0), j = 1, 2, …, t,
γ
α
ˆ
γˆ j
β
j

the following equation gives the definition of an optimised Lasso parameter estimate.
γˆ

*( n )(lasso) = argmin b −
2

γ

t

∑
j =1

2

a j γ j + µm

t

∑θˆ γ
j =1

j

(2)

Let each factor variable A(0) = {A(0)(i), i = 1, 2, …, m} be a non-negative monotonic
raw data series, and the grey prediction model of students’ cognitive states is constructed based on the following steps. Firstly, the cumulative sequence A(1) = {A(1)(l),l = 1, 2, …, m} is obtained by performing one accumulation on A(0). The following equation gives the first order linear differential equation constructed based on A(1).
dA(1)+β A = v
dp(3)vˆ  ˆ vˆ
Aˆ (1) (l + 1) =  Aˆ (1) (1) −  o − βl +

βˆ
βˆ 

(4)

(1)

The single-series first order linear differential equation model is obtained as acumulative quantity. Hence, after the cumulative reduction process, the resulting data
Â(1)(l + 1) can be reduced to Â(0)(l + 1), the grey prediction model for the corresponding original sequence of influence factor data A(0) is expressed asvˆ  ˆ
ˆ
Aˆ (0) (l + 1) = (o − β − 1)  A0 (m) −  o − βl
βˆ 


(5)

The above modelling process leads to Â(0) and the residuals. Make the variance ofA(0) and the residual sequence O denoted by R12 and R22 respectively, which results inthe following
m

R12 =

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2

1
[ a (0) (l ) − a ]m l =1

∑

(6)

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

m

2
1
R =
o (l ) − o ][m l =1
2
2

Based on x 

n

∑
n

(7)
m

m

1
1
1
1
x ( 0 ) (k ), e e (k ), a a ( 0 ) (l ), o o (l ), we
n k 1n k 1m l 1m l 1

could further calculate the posterior difference ratio D = R2/R1 and the small error
probability FR = FR{|o(l) – ō| < 0.75R1}.

3Structural analysis of expertise networks for students’cognitive states

Traditional approaches to knowledge network structure analysis ignore the type andcontent of knowledge expertise and only analyse the network topology. In this paper,
we first described the categories of knowledge nodes within the expertise network forstudents’ cognitive states, and then analysed the knowledge network structure fromtwo perspectives: pathways and statistical characteristics. Figure 3 shows a schematic
diagram of the expertise network architecture.

Fig. 3. Schematic diagram of the expertise network architecture

3.1

Description of expertise categories

The constructed expertise network is built based on n expertise nodes belonging tom majors. The expertise nodes are labelled as unknown or perceived by the students.
As the nodes of the expertise network are majors, they cannot accurately characterise
the knowledge flow between majors if they cannot be categorised as a specific major.
The statistical results of expertise can be characterised by an m × m network adjacency
matrix:258

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States x1,1x
H '   2,1
 xm,1


x1, 2
x2, 2

xm, 2

x1, m 
 x2, m    xm, m 

(8)Assume that the number of associations in which the j-th student’s unknown expertise is associated by the i-th student’s known expertise is denoted by xij (i, j = 1, 2, …, n).
To make it easy to construct connections between expertise and students, the main
diagonal element of the matrix is made to be the number of associations of intraexpertise, often the maximum value of the elements of each row and column of thematrix. It can be assumed that the number of connections to intra-expertise is maximum
for each major. Suppose that the intensity of knowledge flow from student-known major
i to student-unknown major j is given by qij (i, j = 1, 2, …, m), the matrix of knowledge
relations between majors can be obtained by dividing each row of the matrix with xij:=H [=qij ] [ xij / xii ](9)It can be shown that H can characterize the knowledge flow among majors, and theknowledge flow intensity can be measured by the set of network weights. Let the network H = (U,P), the set of network expertise nodes is denoted by U = {u1, u2, u3, …, uM},
the set of edges is denoted by P = { pij|i, j = 1, 2, …, M }, and the number of nodes
in the network is denoted by M = |U|. The number of edges connected to nodeui (i = 1, 2, …, M) is characterized by its degree li, with the entry degree denoted by linl
and the exit degree denoted by liout. The degree of entry is used to characterize the knowledge inflow relationship, while the degree of exit is used to characterize the knowledge
outflow relationship. The weighted network can be represented by H = (U,P,Q), whereQ = {qij|i, j = 1, 2, …,  M} and the weights of edge pij are denoted by qij.
3.2

Path-based analysis of the structure of expertise networks

In this paper, the network is structurally analysed from the perspective of the paths
between the knowledge nodes of the expertise network for students’ cognitive states.
We selected the structural analysis indicators such as the average shortest path anddiameter, the mediated nature of the network and the efficiency of the network for thatstructural analysis.
The length of the shortest path between nodes i and j in an expertise network is thenumber of connected edges on the shortest path, which is denoted by cij. The weightsof the weighted network use the harmonic mean and are denoted by ccij = qilqlj/(qil+qlj).
The mean value of the shortest path between nodes in the expertise network is denotedby K. It is clear that in an expertise network for students’ cognitive states, the amount of

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association between two expertise nodes will gradually become larger as the student’s
learning process progresses. Also, the possibility of knowledge flow increases gradually. Assuming that the set of connected edges on the shortest paths of nodes i and j isdenoted by Eij, the expression for the weights iscijc = 1 /1∑q

qE ∈Eij(10)e

The failure of a node may cause a change in the shortest distance through that node.
In this paper, the number of shortest paths through a node is measured using a betweenness. Assuming that the number of shortest paths between nodes j and s is denoted by
mjs and the number of shortest paths between j and s through node i is denoted by mjs (i),then we defined:
M

yi =

∑

j , s =1, j ≠ lm js (i )

(11)

m js

The distance between knowledge nodes is an important factor affecting students’ability to perform expertise students and thus obtain cognitive state enhancement. In
this paper, we characterized students’ cognitive states enhancement ability through network efficiency. Assuming that the shortest path length between nodes i and j is represented by cij, we have the network efficiency calculation equation as follows:

G (H ) 

g
i j

ij

m (m  1)

11m (m  1) i  j cij



(12)

Suppose the set of connected edges on the shortest path of nodes i and j is denotedby Kij and the weights of any edge on the corresponding path are denoted by qs. Introducing the weights, we have:
G (H ) 


1
1 

m (m  1) i  j  qs Kij qs 



 

(13)

Assuming that the expertise network after removing node ui and its connecting edges
is represented by H\ui, the network knowledge node efficiency is defined as below
based on the network efficiency of the above equation:
G (ui )  G ( H )  G ( H \ ui )

260

(14)

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

3.3

Analysis of the structure of expertise networks based on
statistical properties

In this paper, the network is structurally analysed from the perspective of knowledge node or edge statistics of the expertise network for students’ cognitive states.
We selected structural analysis indicators such as density and average degree of the
network, degree distribution and relevance, hierarchy and circularity.
This paper quantifies the number of connectivity relationships between nodes in a
network based on density and average degree. Assuming that the number of edges in a
network of expertise is denoted by |P|, the density of the network is denoted by N, andthe average degree of the network is denoted by 〈l〉, the value of N is obtained as below
by comparing |P| with the number of possible edges:
N

2P

(15)

m (m  1)〈l〉 is obtained by calculating the average value of degree l:
l 

1
m

m

l
i 1

(16)

i

Assume that the density of the expertise network H is denoted by NH , the densityof the strongly connected network W is denoted by NW , and the average degree of H
is denoted by 〈l〉H. To characterize the distribution status of the node degrees of the
network, this paper defines the node in-degree distribution E(lin) and the out-degree distribution E(lout) of H. E(lin) and E(lout) are usually represented by a cumulative degree distribution function to eliminate the effect of network size. Assuming that the probability
distribution of expertise nodes with degree not less than l is represented by El, we haveEl 

 E (l )

(17)

l 'l

This paper quantifies the relevance of the network based on the Pearson correlation
coefficient. Assuming that the number of edges of the network is represented by NED
and the degree of the two nodes of the i-th edge is represented by ji and li, we have thecorrelation coefficient calculation formula:

s

N ED

1



2

1

j l   N ED 1
 ji  li 
i i i
i 2


2
1 2 2 1
ji  li  ji  li    N ED 1


i 2
i 2



N ED 1







(18)

The closeness of the connection between neighbours of an expertise node can bequantified by the node aggregation factor, and denoted by Di. Assuming that the numberof edges between neighbours of node i is denoted by Ki and the number of neighbouring
nodes by li, the formula is:

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Di =

2K
li (li − 1)(19)

The aggregation coefficient of the whole expertise network is the mean of the aggregation coefficients of all nodes and satisfies D = ∑Di/M. To better measure the relationship between the network nodes and other nodes, this paper introduces the node local
loop coefficient metric. Suppose the degree of node i is denoted by li, any neighbouring
node pair of node i is denoted by 〈kn〉, and the length of the minimum circle throughnode i and its neighbouring nodes k and n is denoted by Rlni , then we have:gi =2li (li − 1)

1

∑R
kn

i
kn

(20)

The recurrent coefficient of the whole expertise network is the mean of the local
recurrent coefficients of the nodes, satisfying G = 〈gi〉.
3.4

Knowledge flow analysis

Since the expertise network for students’ cognitive states is considered as a directed
weighted network. Assuming that the ratio of out- and in-degrees of nodes is represented by hi, the ratio of outgoing and incoming weights is represented by hi′, the sum ofoutgoing and incoming weights of node i is represented by ∑qij and ∑qji, and the position of nodes in the expertise network can be determined based on hi and hi′, we have:l out
hi = i in
li

(21)

q

j
hi 


j

4

ij

q ji

(22)

Experimental results and analysis

Figure 4 shows the distribution of students’ cognitive levels across the different
online learning stages. As can be seen from the figure, the samples used to predict students’ cognitive levels is mostly concentrated at Level 2, Level 3 and Level 4, whichindicates that most students at different online learning stages are able to have a goodunderstanding of the expertise they are studying and can explain some relevant common professional issues. Overall, the cognitive levels of students at different online
learning stages generally conform to the overall distribution. The figure also indicatesthat students at all cognitive levels show an ability growth as they progress through therecommended stages of study.

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

The average betweenness of nodes in the expertise network was calculated to be
50.197, i.e., each expertise node can be considered to be a node on approximately
50.197 shortest paths. Table 1 shows the ranking of the course sub-network nodes interms of their betweennesses, with the expertise nodes BE-1 to BE-10 playing a more
important role in the expertise network in terms of knowledge connectivity comparedto the other nodes.

Fig. 4. Distribution of students’ cognitive levels at different learning stages

Table 1. Ranking of betweennesses for course sub-network nodes
Sub-Network Node Number

Betweenness

Sub-Network Node Number

BetweennessBE-1

362.591BE-6

249.617BE-2

285.427BE-7

248.526BE-3

271.963BE-8

234.158BE-4

265.681BE-9

226.952BE-5

253.627BE-10

193.625

Table 2. Efficiency ranking of course sub-network nodes
Sub-Network Node Number

Efficiency

G(ui)/G(H)EF-1

0.00325

0.092

EF-2

0.00262

0.069

EF-3

0.00245

0.051

EF-4

0.00232

0.044

EF-5

0.00158

0.037

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Table 3. Degree ranking of course sub-network nodes
Sub-Network Node Number

DegreeSub-Network Node Number

Degree

DE-1

94

DE-6

65

DE-2

85

DE-7

53

DE-3

81

DE-8

49

DE-4

74

DE-9

44

DE-5

71

DE-10

21

(a)

(b)

Fig. 5. Distribution of node degrees for different course sub-networks

Table 2 shows the efficiency ranking of the course sub-network nodes, with the thirdcolumn showing the reduction ratio in the efficiency of the course sub-network after
removing the expertise node ui, i.e., the extent to which this expertise node affects the
network efficiency of the expertise network. EF-1 has the highest network efficiency of
0.00325. The failure of this expertise node would result in a reduction in network efficiency of more than 9.2%. Table 3 shows the degree ranking of the course sub-network
nodes. The expertise nodes in the table have a more frequent knowledge flow with othercourse sub-network nodes.
Figure 5a gives the cumulative distribution of out- and in-degrees for course
sub-network 1 in a double logarithmic coordinate system. Figure 5b gives the cumulative degree distribution for course sub-network 2 in a double logarithmic coordinate system. From Figure 5, the out-degree and in-degree distributions of course sub-network 1
and the tail of the degree distribution of course sub-network 2 can be judged to obey thepower-law distribution. Analysis reveals that the out-degree of course sub-network 1
obeys a power-law distribution with an exponent of 1.862, the in-degree obeys a power-law distribution with an exponent of 2.334, and the degree distribution of course
sub-network 2 obeys a power-law distribution with an exponent of 2.077. The above
distribution results indicate that some course sub-networks in the expertise network
have very large out- or in-degrees with other course sub-networks, i.e., there is more
frequent knowledge flow with each other, and there are also course sub-networks that
only have knowledge exchange with a few course sub-networks. Course sub-network 2264

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

is a strongly connected network, and the results of the inter-degree correlation analysis
are given in Figure 6. As can be seen from the figure, the interrelationships between thenodes in course sub-network 2 are more obvious.

Fig. 6. Inter-degree correlation of the course sub-network

Fig. 7. Distribution of local circulation coefficients for the course sub-network

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Figure 7 illustrates the distribution of local circulation coefficients for the course
sub-network. The figure reveals that the local loop coefficients of the nodes in theexpertise network take values in the range of [0.1,0.35], with over 80% of the nodes in
the range of [0.2,0.35], while the loop coefficient of the expertise network is 0.291. This
verifies that the constructed expertise network contains a large number of sub-networks
and loops.

5

Conclusion

This paper analyses the categories and structures of expertise for students’ cognitive
states, and presents in detail a grey prediction algorithm to identify students’ cognitive
states. Then, the paper presents a typological description of the knowledge nodes in theexpertise network for students’ cognitive states, and analyses the knowledge network
structure from the perspectives of paths and statistical properties. After that, the paper
gives a method for analysing the knowledge flow of the expertise network. The experimental results present the distribution of students’ cognitive levels at different learning
stages, while ranking the course sub-network nodes according to their betweenness,
efficiency and degree, along with the corresponding analysis results. The paper alsopresents the cumulative distribution of out- and in-degrees of the course sub-networks
in a double logarithmic coordinate system. The distribution shows that some of thecourse sub-networks in the expertise network have very large out- or in-degrees
with other course sub-networks, i.e., there is a more frequent knowledge flow witheach other. By presenting the distribution of the local loop coefficients of the course
sub-networks, the paper verifies that the constructed expertise network contains a large
number of sub-networks and loops.

6

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optimization model of sports industry structure based on knowledge mapping. Computational Intelligence and Neuroscience, 2022: 1158509. https://doi.org/10.1155/2022/1158509[19] Liu, H., Chen, H., Hong, R., Liu, H., You, W. (2020). Mapping knowledge structure andresearch trends of emergency evacuation studies. Safety Science, 121: 348–361. https://doi.
org/10.1016/j.ssci.2019.09.020[20] Kapuza, A., Koponen, I.T., Tyumeneva, Y. (2020). The network approach to assess the
structure of knowledge: Storage, distribution and retrieval as three measures in analysing
concept maps. British Journal of Educational Technology, 51(6): 2574–2591. https://doi.
org/10.1111/bjet.12938

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Paper—An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States[21] Liu, H., Li, Y., Chen, J., Tao, Y., Xia, W. (2020). A structure mapping–based representation of knowledge transfer in conceptual design process. Proceedings of the Institutionof Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(3): 400–420.
https://doi.org/10.1177/0954405419883070[22] Hou, Q. (2021). Research on the diagnosis of students’ cognitive level based on
deep learning. Security and Communication Networks, 2021: 6106955. https://doi.
org/10.1155/2021/6106955[23] Xu, Z., Jiang, S. (2022). Study on personalized recommendation algorithm of online
educational resources based on knowledge association. Computational Intelligence and
Neuroscience, 2022: 2192459. https://doi.org/10.1155/2022/2192459[24] Jia, H., Yang, L., Cui, B. (2022). Collaborative filtering recommendation of online learning resources based on knowledge association model. International Journal of Emerging
Technologies in Learning (iJET), 17(2): 158–172. https://doi.org/10.3991/ijet.v17i02.29013[25] Prasetya, D.D., Hirashima, T., Hayashi, Y. (2020). Study on extended scratch-build concept map to enhance students’ understanding and promote quality of knowledge structure.
International Journal of Advanced Computer Science and Applications, 11(4): 144–153.
https://doi.org/10.14569/IJACSA.2020.0110420

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AuthorMingzhe Niu, female, now a doctoral student and associated professor at theInnovation and Entrepreneurship school of the Zibo Vocational College of China,
chief of the Innovation and Entrepreneurship Teaching Research Office, and directorof the Entrepreneurship Research Institute; dedicated in the research of innovation andentrepreneurship education, education management, and international education ofChinese language for more than 20 years; writer of more than 10 published papers and2 monographs, and host of 5 municipal-level and above scientific research programs.
Article submitted 2022-09-30. Resubmitted 2022-12-06. Final acceptance 2022-12-07. Final version
published as submitted by the authors.

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Short Paper—Conceptual Statistical Assessment Using JSXGraph

Conceptual Statistical Assessment Using JSXGraph
https://doi.org/10.3991/ijet.v18i01.36529

Curtis Hooper(*), Ian Jones

Loughborough University, Loughborough, UK
C.Hooper@lboro.ac.uk

Abstract—Traditionally online assessments tend to focus on topics that
require students to input algebraic and numeric responses. As such there is a paucity of questions that test students’ knowledge of statistics, and what questions
there are in our experience focus on computing specific values (mean, standard
deviation, and so on). Through making use of a technology called JSXGraph thatis supported within the STACK environment for online assessment of mathematical knowledge, we have developed statistics questions that aim to test conceptual knowledge. For example, by requiring students to adjust the bars in agraph in order to produce a dataset that has a required mean, median, mode andrange. With careful design this approach enables open-ended questions that havemore than one correct answer. In this paper we describe the questions we havedesigned, and report responses from a sample of students.
Keywords—statistics, conceptual understanding, online assessment

1

Procedural and conceptual understanding

A long-standing distinction in the mathematics education research literature is
between procedural and conceptual understanding [1]. The former is often defined interms of facts and procedures, and tends to be straightforward to assess [2]. For example, if we want to know if a student can calculate the arithmetic average (mean) given
a set of numerical data we can ask them to do so and check their answer. Conceptual
understanding is often defined in terms of knowledge of fundamental mathematicalideas and how they connect together. This can be difficult to assess, especially in online
contexts, as it does not lend itself to traditional test items [3].
At Loughborough University we have had success developing online tests for assessing procedural knowledge of statistics. For example, the authors have worked with colleagues from Loughborough’s School of Business and Economics to develop test items
such as that shown in Figure 1. Such items have been used to assess the proceduralknowledge of hundreds of students across various statistical topics such as hypothesis
testing, sampling, and regression. Online assessments can also be used to support student learning of statistical procedures [4], which is important given the reported student
difficulties in this area [5]. For example, we recently embedded test items in online
interactive worksheets as part of the Helping Engineers Learn Mathematics (HELM)
project developed by the universities of Loughborough and Edinburgh [6]. These online
resources allow students to practice statistical procedures, and receive instant feedback.

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Fig. 1. A test item for assessing students’ procedural knowledge of correlation
coefficients in a business context. (Designed by the first author)

Assessments of students’ conceptual understanding of statistics often focus on interpreting (rather than computing) results, and their automation is challenging. For thisproject we turned to the ingenious paper-based learning activities developed by thelate, great mathematical educator Malcolm Swan. In particular, we were inspired by
Swan’s [7] collaborative card-based activities, see Figure 2, which are used in lecturesby the second author. Swan’s intention is for students to discuss in small groups how
the representation of data as bar charts and as summary statistics (mean and so on)
relate to one another. Successfully matching the cards requires thinking strategically,
qualitatively, and quantitatively about different representations of mathematical objects
and this promotes conceptual understanding [8].
During the global pandemic that started in 2020 the second author developed online
drag-and-drop versions of these activities for remote teaching, see Figure 3. However,the drag-and-drop versions were sub-optimal because they did not work well throughmobile browsers as, once the statistics are dragged onto the bar charts the latter could
no longer be seen, the cards could not be written on by students to fill the blanks,and different versions (e.g., with varied datasets, or similar questions addressing related
topics) could not readily be produced. We therefore sought a technology that could be
harnessed to address these limitations.

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Fig. 2. Paper-based learning activities designed to promote conceptual understanding of statistics, in this case averages and range.
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Fig. 3. Drag-and-drop version of Swan’s [7] statistical learning activity

2

STACK and JSXGraph

The program we have used to create such resources is the System for Teaching andAssessment using a Computer Algebra Kernel, better known as STACK [9]. It is available for the Moodle Virtual Learning Environment and can produce online mathematics questions that can be used as standalone learning resources or part of a formal
assessment.

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Short Paper—Conceptual Statistical Assessment Using JSXGraph

A key feature of STACK is to generate randomised question variants within theconstraints set by the question author. This creates a more rigorous test environment
when such questions are used as part of a formal assessment as the correct answers
from one of test will not necessarily be the correct answers to another test, thus making
it harder for cheating to occur.
Another useful feature in STACK is that questions with both numerical and algebraic answers can be marked. Furthermore, feedback can be given instantly on completion of a question. This can range from a simple “correct” or “incorrect” statement, to amore tailored response which is dependent on the answer given. Fully worked solutions
to the question can also be displayed. Students can reflect upon the feedback received
and, if permitted by the author, re-attempt the question. As the feedback is immediate,
the marking of formal assessments set through STACK is far more efficient than marking the test would otherwise be with a human, which would require significantly moretime and money.
Recently, it has been possible to implement JSXGraph into STACK thanks to its
compatibility with Moodle. JSXGraph is a web-based JavaScript program which has theability to plot interactive graphs and mathematical functions [10]. This allows graphs tobe plotted and edited by the student in real time through the use of, for example, sliders
which control certain parameters of the function plotted. When implemented into a
STACK question, the value of the slider can be submitted as the answer to a questionand subsequently marked by STACK in the usual manner.

3

Conceptual statistics test questions

We now present the questions that we constructed by implementing the JSXGraphfeature into STACK. The aim was to create interactive learning resources that students
could use in a learning environment, such as during a lecture or in their own study time,
to improve their understanding of statistics.
An example of such activity is shown in Figure 4. Here the students are given themean, median, mode and range of some unknown data and their task is to move thetwo moveable bars in the bar chart such that the chart conveys the given statistics.
The bars themselves are controlled by a slider which, in Figure 4, are set by default to anumerical value of 8. The student can simply click and hold the white circle to drag thebar to the desired position. The value of the slider will be displayed in the appropriate
answer box as the student moves them. It will be these values that will be scored by
STACK when the student submits their answer.

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Fig. 4. An interactive bar chart using JSXGraph embedded in STACK

When presented with this task, the student must carefully consider what each statistic means and how it relates to the bar chart. A particular case that can arise when there
are two moveable bars is the possibility of multiple correct answers. This is the case in
the question presented in Figure 4. Indeed, to satisfy the condition of the mean equaling
3 we arrive at the equation
11  2 x  4 y 3,5 x y(1)

Where x and y are the user answers corresponding to “Frequency score of 2” and“Frequency score of 4” respectively as given in the question. This leads to the condition
for a correct mean ofy = x + 4.

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Short Paper—Conceptual Statistical Assessment Using JSXGraph

It can be seen that, if this condition is used correctly, the mode will always be 4 asrequired and the range will be 3 accordingly. If students did not take this algebraic
approach to solve the question, the feedback given on a correct answer asked them toconsider if there were any other correct answers. It was hoped then that students would
try to find other solutions thus getting a better understanding of the question.
An alternative question that we made was one which involved the students moving
two nodes connected by a straight line. The task was to draw the correct regression line
for data shown in the form of a scatter plot. An example of this question type is shownin Figure 5.

Fig. 5. An interactive straight line on a scatter graph using JSXGraph embedded in STACKAs the students move the nodes on the sliders, the y–intercept and gradient of theline (given by c and m respectively in Figure 5) are displayed in real time. As before, thevalues of the sliders are displayed in the boxes s1 and s2 respectively and are the values
that will be scored on submission. Here, it is highly unlikely that the student will draw
the true regression line, so it is important for a suitable tolerance to be determined andset accordingly within the coding of the question by the author. The discretisation of
the sliders must also be carefully considered here as making the intervals of the slider
smaller increases the number of possible answers that the student can give, hence making it more difficult for them to draw the true regression line.

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4

Student responses to the test questions

We explored the performance of JSXGraph-based conceptual questions as compared
to STACK-based procedural questions using opportunistic data from a practice (nongraded, optional) test administered as part of a mathematics refresher module. The test
was attempted by 37 students in their first year of university. The test included fiveconceptual questions, all of which required students to drag one or two bars, similar tothe question shown in Figure 4. The test also included five procedural questions thatinvolved calculating the mean, median and/or mode for a presented set of data. The
items contained between 1 and 3 responses each, shown as number of parts in Table 1,
and each was scored out of a total maximum score of 1.
Table 1. Items, number of parts, and scores (max for each item = 1)
Item Name

Number of Parts

Mean Score

Conceptual items
C1

2

0.82

C2

2

0.73

C3

2

0.75

C4

2

0.70

C5

3

057

Procedural items
P1

2

0.95

P2

2

0.89

P3

2

0.91

P4

3

0.93

P5

1

0.85

We conducted a series of analyses to evaluate the validity of the conceptual items.
First, we investigated the internal consistency of each set of items by calculating Cronbach’s alpha, using the guideline that a value ≥ 0.70 indicates satisfactory internal consistency [11]. For the conceptual items we found α = 0.81 and for the procedural items
we found α = 0.73. This suggests satisfactory internal consistency for both sets of items,
meaning each set can be considered to measure a unidimensional construct.
Second, we calculated a total conceptual score and a total procedural score for each
student, and then calculated the Spearman Rank Order correlation coefficient between
them. We found a modest and significant correlation, ρ = 0.50, p = 0.01, suggesting thetwo sets of items assessed related but not identical constructs. This result is consistent
with all the items assessing statistical knowledge, but differing as to whether the focus
is on conceptual or procedural understanding [3].
Third, we explored students’ performance on each set of items, as shown in themean score column in Table 1. Overall, the students scored highly on the test, and all
the procedural item scores (range 0.85 to 0.95) were higher than all conceptual item
scores (range 0.57 to 0.82). A Wilcoxon signed-rank test showed this difference was276

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Short Paper—Conceptual Statistical Assessment Using JSXGraphstatistically significant, p = 0.01. This analysis of student performance is consistentprocedural understanding being generally more accessible by students than conceptual
understanding [1].
Taken together, the above series of analyses provide supportive evidence that the
JSXGraph-based items validly assessed conceptual understanding of statistics.

5

Conclusion

We have demonstrated how the use of an innovative technology, namely JSXGraphembedded in the STACK system, can be used to develop items for assessing students’conceptual understanding of mathematics, at least for the case of simple statistics.
We achieved this by taking research-informed paper-based tasks in the mathematics
literature [7], and adapting them into items that can be automatically marked within a
Virtual Learning Environment. To investigate the validity of the items we calculated
internal consistency, and compared an opportunistic sample of students’ performanceon the items to their performance on procedural items. Our results provided supportive
evidence that the JSXGraph-based items validly and successfully assessed conceptual,rather than procedural, understanding of simple statistics.
We are currently conducting further developmental work and analyses of student
responses towards generating a suite of items that automated the assessment of conceptual understanding across a range of statistical topics and contexts.

6

Acknowledgement

This work was funded by a Teaching Innovation Award from Loughborough
University, UK.

7

References[1] R. Skemp, “Relational understanding and instrumental understanding”, Mathematics Teaching, vol. 77, 1976, Accessed: Jan. 02, 2013.
[2] J. R. Star, “Reconceptualizing procedural knowledge”, Journal for Research in Mathematics
Education, vol. 36, pp. 404–411, 2005.
[3] N. M. Crooks and M. W. Alibali, “Defining and measuring conceptual knowledge inmathematics”, Developmental Review, vol. 34, no. 4, pp. 344–377, Dec. 2014. https://doi.
org/10.1016/j.dr.2014.10.001[4] S. Krishnasamy, L. S. Ling, and T. C. Kim, “Improving Learning Experience of Probability
and Statistics using Multimedia System”, International Journal of Emerging Technologies
in Learning (iJET), vol. 15, no. 01, pp. pp. 77–87, Jan. 2020. https://doi.org/10.3991/ijet.
v15i01.11349[5] N. Chandrasena Premawardhena, “ICT in the foreign language classroom in Sri Lanka: A
journey through a decade”, 10th World Conference on Computers in Education (WCCE2013), Nicolaus Copernicus University, July 2–5, 2013, Torun, Poland. pp. 223–224.

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Short Paper—Conceptual Statistical Assessment Using JSXGraph[6] K. Zerva, I. Nicholson, and A. Mateo, “Translating the HELM workbooks to STACK”,
STACK Assessment, 2021. [Online]. Available: https://stack-assessment.org/CaseStudies/
2021/HELM/. [Accessed Oct. 21, 2022].
[7] M. Swan, Improving Learning in Mathematics: Challenges and Strategies. Department for
Education and Skills (UK), Standards Unit, 2005.
[8] M. Swan, “Learning GCSE mathematics through discussion: what are the effects on
students?”, Journal of Further and Higher Education, vol. 30, no. 3, pp. 229–241, 2006.
https://doi.org/10.1080/03098770600802263[9] C. Sangwin, Computer Aided Assessment of Mathematics. Oxford University Press, 2013.
https://doi.org/10.1093/acprof:oso/9780199660353.001.0001[10] M. Gerhäuser, B. Valentin, and A. Wassermann, “JSXGraph-Dynamic Mathematics withJavaScript”, International Journal for Technology in Mathematics Education, vol. 17, no. 4,
2010.
[11] L. J. Cronbach, “Coefficient alpha and the internal structure of tests”, Psychometrika,
vol. 16, no. 3, pp. 297–334, 1951. https://doi.org/10.1007/BF02310555

8

Authors

Dr Curtis Hooper is a University Teacher in the Wolfson School of Mechanical,
Electrical and Manufacturing Engineering at Loughborough University. He supports
module leaders across the University with the development and implementation of
STACK questions in their modules. (email: C.Hooper@lboro.ac.uk).
Dr Ian Jones is a Reader in Education Assessment in the Department of Mathematics Education at Loughborough University, UK. He was a Royal Society Shuttleworth
Education Research Fellow, sits on the editorial boards of Research in Mathematics
Education and the International Journal for Research and Method in Education, andhas won numerous awards and grants for his work on assessment and other areas ofeducation. (email: I.Jones@lboro.ac.uk).
Article submitted 2022-08-27. Resubmitted 2022-10-29. Final acceptance 2022-11-03. Final version
published as submitted by the authors.

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Short Paper—Analysis of STACK Answer Data Using Pen-Stroke Data from a Calculation Notebook…

Analysis of STACK Answer Data Using Pen-Stroke Data
from a Calculation Notebook and Item Response Theory
https://doi.org/10.3991/ijet.v18i01.36531

Yasuyuki Nakamura(), Kazusa Fujimoto, Kako Ito
Nagoya University, Nagoya, Japan
nakamura@nagoya-u.jp

Abstract—One of the most significant features of STACK, an automatic
assessment system for mathematics, is that it can automatically classify correct,
semi-correct, and incorrect answers using a Potential Response Tree and provide
appropriate feedback for each. However, answers provided by students are only
the results of their calculations, and if they are semi-correct or wrong, it is necessary to check the notes describing the calculations to determine where mistakes
have been made. We developed a function that allows students to submit theirnotes at the same time as their STACK answers, thereby creating an environmentthat allows teachers to check where students have made mistakes and where they
have stumbled. The note submission can be in the form of saving the results ofcalculations made with a digital pen on a tablet. This study examines a methodof analysing answer data by visualising and analysing pen-stroke data from notes
submitted using the latter method and linking this to correct/incorrect data. Frompen-stroke data, two types of visualisation were achieved on the submitted notes.
One was to colour-code the strokes according to the writing speed of the digitalpen, and the other was to display the strokes as thicker if the stagnant time during
which nothing was written was above a threshold value. The difficulty of the
problem was surmised from those data. Then, item response theory was used toverify whether the difficulty was correctly estimated.
Keywords—automatic assessment system for mathematics, pen-stroke data
analysis, item response theory

1

Introduction

In recent years, the development of information technology has accelerated informatisation in the education sector, resulting in more attention being paid to e-learning
than ever before, as lectures are delivered online and home study is conducted with
on-demand materials. One important function of e-learning is online testing, but the
majority of test formats are true/false, multiple-choice, or numerical input. However, foronline tests of science and mathematics, when the answer method is multiple-choice, it
is possible to guess the answer to some extent from the options, and even if the calculation method is not known, it may be possible to guess the correct answer. For example,

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Short Paper—Analysis of STACK Answer Data Using Pen-Stroke Data from a Calculation Notebook…

in integral questions, it is possible to find the correct answer by differentiating all thefunctions given as options, even if one does not understand how to calculate the integral. Where answers are multiple-choice, it may be possible to assess overall ability byassigning a large number of questions, but it is difficult to ascertain exactly how wellstudents understand individual questions. On the other hand, in the case of an answer
format in which formulae are entered, it is expected that the actual ability of the respondent can be measured as it is impossible to answer a question if the calculation method
is not understood. In the case of a formula input format, it is also expected that it ispossible to obtain information on areas where students who have taken the online test
lacked sufficient understanding by analysing wrong answers.
Automatic formula scoring systems include STACK [1], Möbius [2], WeBWorK [3]and Numbas [4]. This paper examines a method for estimating students’ abilities using
answer data from STACK, which is the most widely used system in Japan. Specifically,
this paper uses the function for submitting notes in which calculations are made to provide answers, which will be introduced in the next section, and features of the answers
are extracted from the written data of the notes to estimate the difficulty level of thequestions and the students’ level of understanding. Furthermore, from the correct andincorrect information from the online test, the ability values of the students who took
the test, and the difficulty level of the questions estimated using item response theory
are calculated with the aim of confirming the validity of the findings from the data in
the notes.
The paper is structured as follows. Section 2 briefly introduces STACK, andSection 3 introduces the methods utilised in this study. Section 4 gives examples of theapplication of these methods to real answer data. Section 5 provides a summary anddiscussion.

2

Brief review of STACKIn STACK and other automatic formula scoring systems, a correct or incorrect evaluation is made by entering a formula as a solution, such as in a problem-solving ordinary differential equation, as shown in Figure 1. If the left-hand side of Figure 1 isthe correct answer and the integral constant is missing, as in the right-hand side of
Figure 1, partial points can be given. This kind of partial point evaluation of answers
can be achieved with a function called Potential Response Tree.

Fig. 1. Example of STACK

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Short Paper—Analysis of STACK Answer Data Using Pen-Stroke Data from a Calculation Notebook…

The potential response tree is an algorithm that enables the classification of student
answers by evaluating the answers step by step and from several perspectives by meansof a dichotomous tree. For example, in the potential response tree in Figure 2, first, at
node 1, it is determined whether the student’s answer satisfies the differential equation
or not, and if it is true, it proceeds to node 2; but if it is false, it is scored as 0. At node 2,
it is determined whether the student’s answer is one that neglects an arbitrary constant,and if true, a partial score of 0.5 points is awarded; but if false, it proceeds to node 3.
At node 3, it is determined whether the student’s answer is a trivial one, and if true, it
is awarded 0.1 partial points; but if it is false, 1 point is awarded because it is a correct
answer.

Fig. 2. An example of a potential response tree of STACK for assessing
the question about ordinary differential equations

3

Pen-stroke data analysis and item response theory

Nakamura and Nakahara developed a function that allows the submission of notebooks containing not only answers but also notes describing the calculation process,which can be linked to and managed in relation to questions and answers [5]. Notes canbe submitted either by attaching handwritten notes with photographs or by writing ona tablet with a digital pen. An example of a notebook written on a tablet with a digital
pen is shown in Figure 3 (left). In the upper left-hand corner of the notebook, there are
buttons for selecting the end of the note, erase all, pen, and eraser. The digital pen isused to write answers on the grid. The pen-stroke data contains the pen-strokes writtenby the students in chronological order. Figure 3 (right) shows a part of the recorded
data. Action indicates the state of the pen, such as writing start, writing in progress,or selecting the eraser; X and Y are the coordinates of the pen nib; Time indicates theUNIX time when the Action was performed.

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Fig. 3. An example of a note submitted and a part of the recorded data

The pen stroke data shown in Figure 3 is used to visualise the writing speed andstagnation points. Based on the writing speed obtained, colour is added for each speed.
The lower the speed, the bluer the colour; the higher the speed, the redder the colour. If
more than 2 seconds have elapsed between the end of a stroke and the start of the next
stroke, the post-stagnation stroke is displayed in bold as a stagnation point. Two seconds
or more is a length that can be perceived as stagnation by humans [6].
According to previous empirical findings, students who rewrite more often are lesscertain, but students who write slower but rewrite less tend to solve problems more
carefully and accurately. However, this assessment is subjective, and so, an objective
evaluation is required. We, therefore, used item response theory to estimate students’ability and question difficulty from the correctness and incorrectness data of the test
and compared them with the findings from the pen-stroke data to check the consistency.

4

Analysis of answer data

The visualisation method described in section 3 was applied to the answer data ofthree students who answered six differential and integral calculus questions. The correct/incorrect data and the number of times the eraser was used by each student for eachquestion are summarised in Table 1. Figure 4 shows the notes of two of the students’answers to Question/Item 3. The number of times the total erasure and eraser wereused in this question was 2 for student 1, 0 for student 2 (left of Figure 4), and 4 forstudent 3 (right of Figure 4). The results for the colour of the letters show that student
2 changed his writing speed frequently. The thickness of the first stroke of the equal
sign also changed. Student 3’s colour did not change much, indicating that the student’s
writing speed is almost constant. The thickness of the letters changes in only one place,and the thickness is not much different from normal. The number of times the eraser
was used was higher for the question with more incorrect answers than for the question
with more correct answers. Student 2 solved the questions after thinking about how tosolve them is indicated by the speed changing rapidly and there being some stagnation.

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Short Paper—Analysis of STACK Answer Data Using Pen-Stroke Data from a Calculation Notebook…

However, student 3 solved the questions after thinking about how to solve them whilewriting the answers, which is indicated by the speed not changing much and there being
little stagnation. It is also possible that the speed and stagnation are related to the way
the intermediate equation is written. It has also been assumed that the difficulty of aproblem corresponds to the number of times it is eliminated. Therefore, Questions/
Items 4, 5, and 6 could be guessed to be relatively difficult. Student 1 is predicted to becompetent, as this student correctly answered question/item 6, which is estimated to be
more difficult, although the student had to redraw it more often.
Table 1. Summary of correct and incorrect results for each question/item and the numberof times the eraser was used in the questions of differentiation, integral, and story
Differentiation
Item1
1/0*

Integral

Item2

e**

1/0

Item3
e

1/0

Story

Item4
e

1/0

Item5
e

1/0

Item6
e

1/0

e
32

Student1

1

0

1

0

1

2

0

5

1

7

1

Student2

0

0

0

0

1

0

0

4

0

12

0

14

Student3

1

2

1

0

0

4

0

3

0

8

0

12

Notes: *Results: 1; correct, 0; incorrect, **Number of times erasers are used.

Fig. 4. An example of a note submitted and a part of the recorded
data for student 2(left) and student 3(right)

Next, item response theory was used to estimate a student’s ability and the difficultyof the problem. The ability values were Student 1 > Student 3 > Student 2, which was
roughly inferred in the previous section. Based on the correct/incorrect data, the discriminative ability a and difficulty b of the equationpi ( ) 11 e

1.7 ai (  bi )(1)

were estimated, where pi(θ) is the probability of a student with ability θ answering question i correctly. Equation (1) with calculated a and b, which are called item response
curves, are illustrated in Figure 5. This figure indicates that questions/items 4 and 6
are more difficult. The results predicted in previous paragraphs are here demonstrated
objectively.

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Fig. 5. Item response curves with calculated a and b

5

Summary

In this paper, which uses STACK, we visualised the solution process, writing speed,
and stagnation points based on pen-stroke data obtained from the solution process ofmathematical problems written on a tablet and also predicted students’ ability and the
difficulty level of problems. The results were matched to the results of item response
theory, and qualitative results were obtained. However, due to the small number of data,
it is difficult to qualify this as a general trend, and it will be necessary to increase thenumber of data and verify this trend in more detail in the future.

6

Acknowledgments
This work was supported by JSPS KAKENHI Grant Numbers 21H04412, 19K21758.

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https://doi.org/10.1093/acprof:oso/9780199660353.001.0001[2] Möbius – DigitalEd. [Online]. Available: https://www.digitaled.com/mobius/. [Accessed
Oct. 31, 2022].
[3] The WeBWorK Project. [Online]. Available: https://openwebwork.org/. [Accessed Oct. 31,
2022].
[4] Numbas. [Online]. Available: https://www.numbas.org.uk/. [Accessed Oct. 31, 2022].
[5] Y. Nakamura and T. Nakahara, “Note-Submission Function for Moodle Quiz and Collecting Pen-Stroke Data,” in IADIS International Conference Mobile Learning 2019, 2019,
pp. 163–164. https://doi.org/10.33965/ml2019_201903P001[6] Masaaki Iiyama, Chihiro Nakatsuka, Yoshitaka Morimura, Atsushi Hashimoto, Masayuki
Murakami, Michihiko Minoh, “Detecting Answer Stuck Point Using Time Intervals of Pen
Strokes”, Jpn. Soc. Infor. & Sys. Edu, vol. 34, no. 2, pp. 166–171, 2017.

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Short Paper—Analysis of STACK Answer Data Using Pen-Stroke Data from a Calculation Notebook…

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Authors

Yasuyuki Nakamura is an associate professor at the Graduate School of Informatics of Nagoya University, Nagoya, 464-8601, Japan.
Kazusa Fujimoto is a graduate student at the Graduate School of Informatics ofNagoya University, Nagoya, 464-8601, Japan.
Kako Ito is a graduate student at the Graduate School of Informatics of Nagoya
University, Nagoya, 464-8601, Japan.
Article submitted 2022-10-03. Resubmitted 2022-11-14. Final acceptance 2022-11-18. Final version
published as submitted by the authors.

iJET ‒ Vol. 18, No. 01, 2023

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iJET − Vol. 18, No. 01, 2023

Imprint
iJET – International Journal of Emerging Technologies in Learning
http://www.i-jet.org

Editor-in-Chief
Dominik May, University of Georgia, United States

Executive Editor
Michael E. Auer, CTI Frankfurt/Main, Vienna, New York, Bengaluru

Associate Editors-in-Chief
Martin Ebner, TU Graz, Austria
Mohammad Khalil, University of Bergen, Norway

Associate Editors
Neelakshi Chandrasena Premawardhena, University of Kelaniya, Sri Lanka
Christina Merl, Vienna, Austria
Jalal Nouri, Stockholm University, Sweden
Stamatis Papadakis, University of Crete, Greece
Valerie Varney, RWTH Aachen University, Germany

Technical Editor
Sebastian Schreiter, Lagorce, France

Editorial Board
A. Y. Al-Zoubi, Princess Sumaya University for Technology Amman, Jordan
Gerald Friedland, International Computer Science Institute, Berkeley (CA), USA
Andreas Holzinger, Medical University Graz, Austria
Dr. George S. Ioannidis, University of Patras, Greece
Andreja Istenic Starcic, Slovenia University of North Texas, US
Barbara Kerr, Concordia University Montreal, Canada
Lars Knipping, TU Berlin, Germany
Massimo Marchiori, World Wide Web Consortium (W3C) at MIT
Andreas Pester, Carinthia University of Applied Sciences, Austria
Rob Reilly, University of Memphis and MIT Media Lab, USA
Cornel Samoila, Transilvania University Brasov, Romania
Mario Žagar, University of Zagreb, Croatia

Indexing
International Journal of Emerging Technologies in Learning is indexed in Elsevier Scopus,
EI Compendex, DBLP, Ulrich, EBSCO, INSPEC, LearnTechLib, and Google Scholar.

Publication Frequency
Bi-Monthly

ISSN
1863-0383

Publisher
International Association of Online Engineering (IAOE)
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Austria