iafor

journal of education

technology in education
Volume 10 – Issue 2 – 2022
Editor: Michael P. Menchaca
Editor-in-Chief: Joseph Haldane

ISSN: 2187-0594

iafor
IAFOR Journal of Education: Technology in Education
Volume 10 – Issue 2 – 2022

IAFOR Publications
The International Academic ForumIAFOR Journal of Education: Technology in Education
EditorMichael P. Menchaca
University of Hawaii at Manoa
Associate Editor
Daniel Hoffman
University of Hawaii at Manoa
Associate Editor
Devayani Tirthali
Research Associate, India
Editor-in-Chief
Joseph Haldane
The International Academic Forum (IAFOR)
Published by The International Academic Forum (IAFOR), Japan
IAFOR Publications, Sakae 1-16-26-201, Naka-ward, Aichi, Japan 460-0008

Executive Editor: Yvonne Masters
IAFOR Journals Administrator: Ruth Mund
Publications Manager: Nick Potts
IAFOR Journal of Education: Technology in Education
Volume 10 – Issue 2 – 2022
Published August, 2022
IAFOR Publications © Copyright 2022
ISSN: 2187-0594
ije.iafor.org

IAFOR Journal of Education: Technology in Education
Volume 10 – Issue 2 – 2022
Edited by Michael P. Menchaca
Associate Editor: Daniel Hoffman
Associate Editor: Devayani Tirthali

Foreword
There can be little doubt that the technology that we have come to rely on today makes andshapes our modern lives. Technology’s role within education over the past few years during
the global pandemic has highlighted its importance and centrality. IAFOR would not be able
to operate and facilitate its global platform without ever-better and more refined tools, and theexample of this finished journal is testament to this.
This Technology in Education issue of the IAFOR Journal of Education is the result of
collaborative human endeavour and technology, as indeed its scope centres on that very
intersection. I would like to thank everyone involved in making this issue possible. The finishedproduct is a truly international effort, involving many different scholars, as authors, as
reviewers, as editors, as senior administrators; and not to forget the dedicated publishing team.
In keeping with the international, intercultural and interdisciplinary mission and spirit of
IAFOR, the articles in this journal are from authors around the world and reference manydifferent fields. Within these pages are examples, findings and results from studies across theworld which readers, drawing on their own contexts, will find of comparative and contrastive
interest.
These articles should be seen as a window to the world, from wherever you happen to be in theworld.
Happy Reading,
Joseph Haldane
Editor-in-Chief
IAFOR Journal of Education

Editorial Advice
Preparing a submission to the IAFOR Journal of Education is more than writing about yourresearch study: it involves paying careful attention to our submission requirements. Different
journals have different requirements in terms of format, structure and referencing style, among
other things. There are also some common expectations between all journals such as the use ofgood academic language and lack of plagiarism. To assist you in reaching the review stage forthis or any other peer-reviewed journal, we provide the following advice which you should
check carefully and ensure that you adhere to.
1. Avoiding Plagiarism
Plagiarism is a practice that is not acceptable in any journal. Avoiding plagiarism is the cardinalrule of academic integrity because plagiarism, whether intentional or unintentional, ispresenting someone else’s work as your own. The IAFOR Journal of Education immediately
rejects any submission with evidence of plagiarism.
There are three common forms of plagiarism, none of which are acceptable:
1. Plagiarism with no referencing. This is copying the words from another source (article,
book, website, etc.) without any form of referencing.
2. Plagiarism with incorrect referencing. This involves using the words from anothersource and only putting the name of the author and/or date as a reference. Whilst not as
grave as the plagiarism just mentioned, it is still not acceptable academic practice.
Direct quoting requires quotation marks and a page number in the reference. This isbest avoided by paraphrasing rather than copying.
3. Self-plagiarism. It is not acceptable academic practice to use material that you havealready had published (which includes in conference proceedings) in a new submission.
You should not use your previously published words and you should not submit about
the same data unless it is used in a completely new way.
2. Meeting the Journal Aims and Scope
Different journals have different aims and scope, and papers submitted should fit the specificjournal. A “scattergun” approach (where you submit anywhere in the hope of being published)
is not sound practice. Like in darts, your article needs to hit the journal’s “bullseye”, it needs
to fit within the journal’s interest area. For example, a submission that is about building bridges,
will not be acceptable in a journal dedicated to education. Ensure that your paper is clearly
about education.
3. Follow the Author Guidelines
Most journals will supply a template to be followed for formatting your paper. Often, there will
also be a list of style requirements on the website (font, word length, title length, page layout,
and referencing style, among other things). There may also be suggestions about the preferred
structure of the paper. For the IAFOR Journal of Education these can all be found here:
https://iafor.org/journal/iafor-journal-of-education/author-guidelines/

4. Use Academic Language
The IAFOR Journal of Education only accepts papers written in correct and fluent English ata high academic standard. Any use of another language (whether in the paper or the referencelist) requires the inclusion of an English translation.
The style of expression must serve to articulate the complex ideas and concepts being presented,
conveying explicit, coherent, unambiguous meaning to scholarly readers. Moreover,manuscripts must have a formal tone and quality, employing third-person rather than firstperson standpoint (when feasible), placing emphasis on the research and not on unsubstantiatedsubjective impressions.
Contributors whose command of English is not at the level outlined above are responsible forhaving their manuscript corrected by a native-level, English-speaking academic prior to
submitting their paper for publication.
5. Literature Reviews
Any paper should have reference to the corpus of scholarly literature on the topic. A review ofthe literature should:
•
•
•
•
•

Predominantly be about contemporary literature (the last 5 years) unless you are
discussing a seminal piece of work.
Make explicit international connections for relevant ideas.
Analyse published papers in the related field rather than describe them.
Outline the gaps in the literature.
Highlight your contribution to the field.

Referencing
Referencing is the main way to avoid allegations of plagiarism. The IAFOR Journal of
Education uses the APA referencing style for both in-text citations and the reference list. If
you are unsure of the correct use of APA please use the Purdue Online Writing Lab (Purdue
OWL), – https://owl.english.purdue.edu/owl/resource/560/01/ – which has excellent examples
of all forms of APA referencing. Please note APA is used for referencing not for the generalformat of the paper. Your reference list should be alphabetical by author surname and include
DOIs whenever possible.
This short guide to getting published should assist you to move beyond the first editorial review.
Failure to follow the guidelines will result in your paper being immediately rejected.
Good luck in your publishing endeavours,
Dr Yvonne Masters
Executive Editor, IAFOR Journal of Education

Table of Contents
From the Editors
Daniel L. Hoffman
Devayani Tirthali
Michael P. Menchaca

1

Notes on Contributors

3

The Link between Internet Connectivity and Missed Assessmentsin the Online Class Modality
Willington Onuh
Olivia M. Legaspi
Susan T. Mostajo
Don S. Malabanan
Rosario T. Reyes

7

The Need for Digital Education in the Teaching Profession: A Path Toward
Using the European Digital Competence Framework in Albania
Heliona Miço
Jonida Cungu

29

Using Computational Thinking to Facilitate Language Learning: A Survey ofStudents’ Strategy Use in Austrian Secondary Schools
Marina Rottenhofer
Lisa Kuka
Sandra Leitner
Barbara Sabitzer

51

A Comprehensive Community of Inquiry Framework for Exploring Technology
Enhanced Language Learning
Irum Alvi

71

Immersive Technologies in Physical Education in Malaysia for Students
with Learning Disabilities
Nur Azlina Mohamed Mokmin
Nurul Nabilah Izzati Binti Ridzuan

91

Using Educational Digital Storytelling to Enhance Multilingual Students’Writing Skills in Higher Education
Eleni Meletiadou

111

Reviewers

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IAFOR Journal of Education: Technology in Education

Volume 10 – Issue 2 – 2022

From the Editors
The current issue comes to press amongst a sense of reflection and renewal within education.
Prompted by the challenges of a global pandemic, many of us are contemplating how to makethe field – and all that it encompasses – more nimble and flexible. The goal, of course, is toimprove teaching and learning for everyone, everywhere. And, for readers of the Journal, it
should come as no surprise that technology, in all its shapes and sizes, figures prominently in
our collective search to improve what we do and how we do it.
It is within this broader context that the current issue's six articles are presented. Two of thearticles focus on issues of education policy and technology. They ask key questions about thereadiness of our educational systems and our ability to enact policies that align with the needsof modern learners. Another set of articles concentrates on the role technology is playing in theprocess of language learning – an ever-present need for life in our interconnected, multiculturalsociety. The final two articles examine specific technology-enabled innovations. These works
describe efforts to test the possibilities of digital tools designed to support diverse learners in
different content areas. Taken together, these manuscripts continue the Journal's ongoingcommitment to high-quality, interdisciplinary scholarship.
To help you get oriented, here is a brief summary of each article in the issue.
In the first article, Onuh and colleagues consider how internet connectivity affects students’capacity to meet assessment and learning expectations. Using a count data model, they provide
evidence that students with poor internet connectivity tend to have higher rates of missed
assessments. Conclusions advanced by the study encourage educational institutions to create
online learning policies that align with the technical realities of the digital age.
Miço and Cungu, in their article titled, "The Need for Digital Education in the Teaching
Profession," analyze the state of digital education in Albania. Employing a survey based on theEuropean Digital Competence Framework, the authors pinpoint a number of specific areas ofeducation in need of improvement such as teacher training and physical infrastructure. The
article concludes by encouraging policy makers to consider ways to support regional andnational efforts to enhance the system’s digital development.
In the third article, Rottenhofer and colleagues share an interdisciplinary study exploring theuse of computational thinking skills to support language learning. Using a multiple-case study
approach, the work presents evidence that secondary language learners were medium to low
users of learning strategies related to computational thinking. In the authors’ view,
strengthening these strategies is an avenue for enhancing language learning skills and preparing
them their future professional lives.
In another manuscript focused on technology and language learning, Alvi shares aninvestigation of the influence of presence on the second language learning experience. Situatedin India, this study extends the Community of Inquiry framework, by proposing a morecomprehensive model that incorporates different forms of presence (e.g., emotional,
technological). Conclusions shared in the paper indicate that accounting for different forms ofpresence should be considered when designing and implementing second language learning
experiences.

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In the fifth article, Mokmin and Ridzuan explore how to leverage immersive technologies tohelp students with learning disabilities benefit from physical education. Drawing on theories
of motor and multimedia learning, the authors describe an application designed to makephysical education more accessible for secondary students with disabilities. In the conclusion,
the authors suggest that carefully designed immersive technologies coupled with appropriate
learning material may extend physical education opportunities to a more diverse group oflearners.
For the final article of the issue, Meletiadou presents a case study exploring educational digitalstorytelling as a mechanism for developing 21st century skills. Detailing findings based on
quantitative and qualitative data, this mixed-methods study provides evidence that digitalstorytelling can help learners improve on a variety of cognitive, affective and interpersonal
outcomes including writing performance, critical thinking skills, self-confidence, andintercultural awareness. The article concludes by discussing the implications of digitalstorytelling and suggesting some implementation strategies for higher education.
Overall, these articles illustrate the quality and variety of education-related scholarship
happening around the world from Austria to the Philippines. Together they represent ourcollective effort (a) to understand how technology is influencing – directly and indirectly – thefield, and (b) to position education to face the known and unknown challenges of the future.
Daniel L. Hoffman and Devayani Tirthali, Associate Editors
Michael P. Menchaca, Editor
IAFOR Journal of Education: Technology in Educationtech.editor.joe@iafor.org

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Notes on Contributors
Article 1:
The Link between Internet Connectivity and Missed Assessments in the Online Class
Modality
Willington Onuh is a Professor of Economics at De La Salle University Dasmarinas-Cavite
Philippines. He earned PhD in Development Studies (major in Economics) from De La Salle
University Manila Philippines in 2004. He was a Visiting Research Fellow at the Institute ofDeveloping Economies (IDE) Chiba, Japan in 2005 and Visiting Professor at the School of
Economics and Business Administration, Moraga California in 2009. He has written andpublished on issues related to health, poverty, education, development and business economics.
Email: willingtonoonuh@dlsud.edu.ph
Dr Olivia M. Legaspi is a full-time professor at the College of Education of De La Salle
University- Dasmariñas (DLSU-D) in Cavite, Philippines, teaching undergraduate andgraduate courses in curriculum and instruction, educational technology, and educational
management, which also serve as her research interests. She obtained her EdD major in
Educational Management degree in DLSU-D. She is an assessor of the Lasallian Schools
Supervision Office (LASSO) and of the Commission on Higher Education (CHED)
Institutional Sustainability Assessment (ISA), a member of the CHED Region IV-A Regional
Quality Assessment Team (RQAT), and an accreditor of the Philippine Accrediting
Association of Schools, Colleges and Universities (PAASCU).
Email: omlegaspi@dlsud.edu.ph
Dr Susan T. Mostajo is a professor and the Senior Director of Human Resource Management
Office of De La Salle University – Dasmariñas (DLSU-D), Cavite, Philippines. She isregistered both as Psychologist and as Guidance Counselor. She obtained her doctorate degreein Counseling Psychology at DLSU-D. She is affiliated with local and international
professional organizations in her field of expertise. Currently, she is serving her third term as
President of the Asian Association of School HRMD Practitioners, Inc. (AASHPI). As a
research advocate, she has presented and published articles in various areas like humanresource management, psychology, education, service science, and experience design.
E-mail: stmostajo@dlsud.edu.ph
Don S. Malabanan taught undergraduate and graduate level finance and economics courses
at the De La Salle University-Dasmariñas in Cavite, Philippines from 2002 to 2022. He holds
a Masters of Science in Computational Finance degree from De La Salle University, Manila,
Philippines. He was the first CITI-FINEX Rafael B. Buenaventura Jr. Outstanding Finance
Educator Awardee for Luzon (excluding NCR). His research and consultancy work focusedon corporate finance, valuation, financial markets, and industry analysis. He has, likewise,
been actively involved in institutional research at the tertiary level.
Rosario T. Reyes is a faculty member of De La Salle University-Dasmariñas, Cavite,
Philippines and teaches Entrepreneurship and Finance. She earned her Master inBusiness Administration degree from the same university and is finishing a dissertation
paper for Doctorate in Business Administration at the De La Salle University Manila,
Philippines.

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Article 2:
The Need for Digital Education in the Teaching Profession: A Path Toward Using theEuropean Digital Competence Framework in Albania
Dr Heliona Miço is a lecturer of Public Law, in the Law department at “Epoka University”
and researcher of human rights and the right to education, accreditation, child's rights and social
justice. Dr. Miço was educated at Faculty of Law in University of Tirana, Albania andgraduated with bachelor degree. She has defended her PhD at the Institute of European Studies,in University of Tirana with the theses “A general overview of the right to education in Albania.
Development of this right in accordance with international standard and European legislation.”
She is involved as a researcher in “Introducing modules on law and rights in programmes ofteacher training and educational sciences: A contribution to building rights-based education
systems in countries in transition” (EduLAW) Erasmus+ Mundus Curriculum Development
project and as a lecturer at the University of Salerno (UNISA), Italy for Teaching Mobility inthe framework of the Erasmus+ Program.
Dr Jonida Cungu is a researcher in the field of lexicology and semantics, and lecturer at the
department of linguistics at the Faculty of Humanities, University of Elbasan “Aleksandër
Xhuvani”. She finished her studies at the University of Elbasan “Aleksander Xhuvani”, in the
Faculty of Humanities, branch of language and literature. She has worked in pre-university
education and higher education. She has completed in-depth university studies ("master"level) in the language department at the Faculty of History and Philology of the University ofTirana, and further doctoral studies in the field of lexicology and semantics. She finished her
doctorate in 2016 and received the scientific degree "Doctor of Science". She is engaged inacademic teaching activities in courses such as: introduction to linguistics, language training,
general linguistics, semiotics. Her academic and scientific activity includes research in thefield of lexicology and semantics, language culture and education.
Article 3:
Using Computational Thinking to Facilitate Language Learning: A Survey of Students’
Strategy Use in Austrian Secondary Schools
Marina Rottenhofer is a university assistant in the department of STEM education at the
Johannes Kepler University Linz, Austria. She is deputy head of the JKU COOL Lab, ateaching and learning lab for everyone focusing on computer science and digital education. In
addition, she has been involved in various national and international projects on the topics ofcomputational thinking, STEM didactics, talent promotion and brain-friendly learning. As partof her doctoral studies, she is investigating the use of computer science models as a teaching
and learning strategy to promote computational thinking skills. Due to her teachingqualification in the subjects English and Italian, her dissertation focuses particularly on
language teaching and learning.
Email: marina.rottenhofer@jku.at
Lisa Kuka started her academic education at the University of Applied Sciences St. Pölten,
Austria with a specialization in Digital Media Technology and later on moved to the Johannes
Kepler University, as well as the University of Art and Design in Linz, Austria for the Teacher
Training Programme specialized in English, computer science and media design education.
Next to teaching media design and applied computer science courses at a College for Higher
Vocational Education, she works as a research and teaching assistant aspiring to gain her PhD

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at the Department of STEM Education at the Johannes Kepler University Linz, Austria. Her
research interests include AI in Education, computational thinking, and digital literacy.
Sandra Leitner is a research associate in the Department of STEM Education at the Johannes
Kepler University in Linz, Austria. In addition, she is a teacher for Spanish, Psychology andPhilosophy and teaches at a secondary school. Furthermore, she is active in the promotion ofgifted students with her training as a European Consul of High Ability (ECHA). Since 2021,
she has been working on her dissertation in the field of STEM education for girls, in which she
investigates how girls become enthusiastic about STEM in creative learning environments andhow to keep them enthusiastic from kindergarten to the workplace.
Dr Barbara Sabitzer has been a professor of instructional technology at the Johannes Kepler
University in Linz, Austria, since 2017. There she is responsible for the training and further
education of computer science teachers as well as for teaching computer science basics forother subjects. In addition, Barbara Sabitzer founded and leads the JKU COOL Lab, which
offers workshops, clubs, advanced training and much more for children, young people andadults on the topics of computer science and digital education. As part of her habilitation, she
worked on effective, brain-friendly teaching and learning methods and developed a flexible
teaching concept (COOL Informatics), which forms the basis of the JKU COOL Lab and herteaching.
Article 4:
A Comprehensive Community of Inquiry Framework for Exploring Technology
Enhanced Language Learning
Dr Irum Alvi is an Assistant Professor at the Department of Humanities, English & Applied
Sciences University Departments, Rajasthan Technical University, Kota, India.
Email: irumalvi@gmail.com
Article 5:
Immersive Technologies in Physical Education in Malaysia for Students with Learning
Disabilities
Dr Nur Azlina Mohamed Mokmin holds a Doctorate Degree (PhD) in Instructional System
Development. Her work focuses specifically on the application of Artificial Intelligent, Virtual
Reality, and Augmented Reality in education. Her current research is on the application of
immersive technologies for medical and health studies. She is currently teaching in the Centre
of Instructional Technology and Multimedia, Universiti Sains Malaysia (USM). Previously she
worked at Malaysian Polytechnics before continuing her service with the government in USM.
As the head of innovation for the center, she has done a lot of teaching, training, and developing
various software projects. She also has won awards for innovation and her university
services.
Nurul Nabilah Izzati Binti Ridzuan holds a Master of Instructional Multimedia and Bachelorof Computer Science (Hons.) Multimedia Computing from Universiti Sains Malaysia andUniversiti Teknologi MARA (UiTM), respectively. Her related coursework are Multimedia
Authoring, Virtual Reality and Computer Graphics (2D and 3D). She is experienced in using
Adobe After Effects and 3ds Max.
Email: nabilahizzatiridzuan@gmail.com

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Article 6:
Using Educational Digital Storytelling to Enhance Multilingual Students’ Writing Skills
in Higher Education
Dr Eleni Meletiadou is a Senior Lecturer (Associate Professor) in Equality, Diversity, andInclusion in Higher Education at London Metropolitan University Guildhall School ofBusiness and Law. She is an enthusiastic peer learning, writing, and assessment scholar andan award-winning researcher and adviser with over 20 years of international experience. She
is the Chair of the European Association for Educational Assessment (AEA-Europe) Inclusive
Assessment SIG and an Expert Member of the European Association for Language Testingand Assessment (EALTA). She is the Conference Track Chair of the British Academy ofManagement Knowledge & Learning SIG and the European Academy of Management(EURAM) Doctoral Accelerator Mentor.
E-Mail: elenim@outlook.com

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The Link between Internet Connectivity and Missed Assessmentsin the Online Class Modality
Willington Onuh
De La Salle University-Dasmariñas, Dasmariñas City, Cavite, Philippines
Olivia M. Legaspi
De La Salle University-Dasmariñas, Dasmariñas City, Cavite, Philippines
Susan T. Mostajo
De La Salle University-Dasmariñas, Dasmariñas City, Cavite, Philippines
Don S. Malabanan
De La Salle University-Dasmariñas, Dasmariñas City, Cavite, Philippines
Rosario T. Reyes
De La Salle University-Dasmariñas, Dasmariñas City, Cavite, Philippines

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Abstract
Many published papers provide insights on factors affecting learning performance; however,they do not address how internet connectivity affects students’ capacity to meet assessment
and learning expectations. To address this gap in the literature, we draw from a survey of 257
students at the undergraduate level to investigate two questions: (a) To what extent doesinternet connectivity affect missed assessments? and (b) How do students vary through the
distribution of missed assessments? We used a count data model, specifically, negative
binomial (NB) regression, to determine incidence rate ratios and odds of missed assessments.
The NB results showed that students who indicated poor internet connectivity during thesemester had about a five times higher incidence rate of missed assessments than students who
did not indicate poor internet connectivity. Surprisingly, despite two-thirds of students
reporting poor internet connectivity, the chance of accumulating seven missed assessments
during the semester was very minimal. The results may provide insights to faculty andeducation policymakers at the institutional level on ways to design online learning to meetlearning expectations.
Keywords: internet connectivity, learning performance, missed assessments, online modality

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Since COVID-19 was declared a pandemic by the World Health Organization (WHO) in thefirst quarter of 2020, there has been no shortage of research papers on how it has transformed
learning in higher educational institutions around the world. Online learning in many parts of
the world, especially in developing countries, faced a number of constraints, such as access tointernet connectivity, financial resources to procure technological devices and physicalenvironment conducive for effective learning (Fishbane & Tomer, 2020; UNESCO, 2020;
Affouneh et al., 2020; Crawford et al., 2020; Limniou et al., 2021). Furthermore, because
online learning is dependent on digital tools, many schools implemented varying digitallearning activities based on resource capacity and platform limitations (Joshi et al., 2020).
While there are a number of new online learning technologies available in the market today toboost both learning and delivery capacity by faculty (Goh & Sigala, 2020), many schools lackthe capacity to procure these new technologies. Even when schools can afford such platforms,
students may not have adequate digital devices or a conducive environment to engage faculty(Arora & Srinivasan, 2020, Agarwal & Kaushik, 2020; Chick et al., 2020).
Experiences in online modalities are not entirely negative. An expansive body of researchshows positive effects of online transition on students (Babbar & Gupta, 2021; Muthuprasad
et al., 2021; Chisadza et al., 2021). Chisadza et al. (2021) identified clear opportunities withrespect to the shift to online learning. Babbar & Gupta (2021) measured benefits of online
learning in terms of innovations in the types of assessments used in higher education.
Muthuprasad et al. (2021) explored students’ preferences for various attributes of online classes
including online learning environment. They found that the main attraction to online classes
are flexibility and convenience brought by online learning. For the most part, it has become
increasingly possible for students to get into classes from the comfort of personalized spaces.
From all indications, digital inequality, described by Beaunoyer et al (2020) as “… access to
networks or connected devices, or when it comes to the skills required to navigate
computerized spaces optimally” (p. 1), remains a problem for online education in developed
and developing countries. Prior studies have shed light on various aspects of the problem ofdigital inequality: sustainability or environmental conditions in Mexico (Vargas et al., 2020);
internet connectivity and socio-economic class in Ireland (Cullinan et al., 2021); internet usage
and academic achievement in Indonesia (Soegoto & Tjokroadiponto, 2018); internet accessand power outage in Nigeria (Ivwighreghweta & Igere, 2014); low income students and online
education in India (Jain et al. (2021); limited laboratory-related courses and internet
connectivity in the Philippines (Rotas & Cahapay, 2020; Cahapay, 2020); academic
performance and access to WiFi in South Africa (Chisadza et al., 2021); and internet
connectivity and lower remote learning proficiency in the USA (Katz et al., 2021).
There has been almost no empirical work on the relationship between internet connectivity andmissed assessments except for Katz et al. (2021) which focused on the association between
internet connectivity and lower learning proficiency in an online modality. Given how theeducation sector around the world was forced to go online and the inherent problem ofbalancing quality and expectations of students’ compliance with online assessments, there is
an important gap to fill in understanding the full range of what might be necessary in designingonline learning. When many schools transitioned to an online modality, the traditional
institutional guidelines governing the conduct of class were formulated for face-to-facecontext. Applying these guidelines without clear understanding of underlying factors driving
student responses to assessments became a problem. Granting extensions to assessments
submitted after a deadline in face-to-face classes is a common issue faculty deal with all thetime. In online settings, missed assessments assume a different dimension because not only are

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digital devices needed, but also internet connectivity is required to drive the virtual meetingand facilitate timely submission of assessments. Understanding how internet connectivity isrelated to the number of missed assessments can serve as a reference for setting guidelines thatgovern expectations in online learning and outcomes as well as the administration of theseassessments.
To contribute to literature, the study investigated two questions: (a) To what extent doesinternet connectivity increase or decrease missed assessments? and (b) How do students varyin the distribution of missed assessments? We use a count data model which allows for discretevalues in regression estimation to examine the relationship between internet connectivity andnumber of missed assessments and analyzed odds of missing assessments. The goal is tounderstand a possible potential allowable number of missed assessments that students may
incur in an online modality without penalty. Overall, our focus on the relationship between
internet connectivity and missed assessments distinguishes our paper from the only similar
work done in the US using a unique data set from 30 universities from 19 states and the Districtof Colombia on internet connectivity and lower Remote Learning Proficiency (Katz et al.,2021).
Prior studies have made enormous contributions to online learning in the literature especiallyin the area of learning outcomes; however, this study may be the first to model missed
assessments of students in an online modality in higher education using a count data model.
We believe that the findings appeal to a broad spectrum of online advocates, readers andeducators including education policymakers.
The rest of the paper is organized as follows: Section 2 describes the data sources and summary
statistics including institutional context of missed assessments, Section 3 gives the empiricalstrategy including detailed theoretical and empirical formulation of the estimation process,Section 4 discusses the main results, and Section 5 offers concluding remarks and draws policy
implications.
Educational Setting and Data
Institutional Context of Assessments and System’s Theory in Education
Assessment is a key process of learning in higher education. In fact, assessment has been
described by Hodges et al. (2014) as “intrinsically linked to student learning and performance”(p. 189). Assessment as an integral part of higher education has been exhaustively studied andtheorized (Hodges et al., 2014) and its role in feedback mechanisms facilitating learning has
been well-documented (Graham et al., 2021). But as educational institutions transitioned fromthe traditional face-to-face to online classes, many schools were faced with two problems
occurring simultaneously: relevant data for suitable guidelines for online classes and adequate
digital infrastructure including stable internet connectivity (Chisadza et al., 2021; El Said,
2020).
There are three types of assessments that are commonly used in varying forms in online
modalities: formative, enabling, and summative. Formative assessment, as the name implies,
involves more frequent informal activities used in between teaching to gauge students’
understanding of lectures and does not count toward grades directly. It is used to prepare
students for either enabling or summative assessments (Burkhardt & Schoenfeld, 2018).
Enabling assessment is periodic and more frequent compared with summative assessment.

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Enabling assessment may be described as mini-summative in the sense that it gauges progressof learning on a much smaller scale at different points within the same module (e.g., consistingof few chapters). It is not uncommon to have two or three in one module depending on thesubject. Examples include multiple choice questions, short essays, debate exercises, etc. On
the other hand, summative assessment is usually designed to gauge overall grasp of the entire
module or multiple modules and may involve written examinations, written research papers or
well-designed projects (Guangul et al., 2020).
Overall, assessment has been described as a good measure of both quality and progress inonline learning (Babbar & Gupta, 2021). However, even when assessments have beendeveloped by experts to elicit a given performance, its usefulness will ultimately depend on
digital infrastructure and home conditions driving communications and feedback in both
directions (students and faculty) for smooth and unrestricted learning to take place (Yan &Carless, 2021). Yet not enough attention has been given to the effect of internet connectivity
on students’ ability to meet online assessment expectations. Assessments help faculty makebetter decisions on students’ progress (Carless & Winstone, 2020); however, when students
are unable to submit assessments in a timely manner due to poor WiFi reception or internet
connectivity, faculty may apply penalties indiscriminately which is contrary to how the
feedback mechanism should work. Kintu et al. (2017) notes that, “efficient use of a learning
management system and its tools improves learning outcomes in e-learning and blended
learning environments” (p. 5). Evidence of the challenges faced in an online modality due tolack of clear cut guidelines governing conduct has been documented (Guangul et al., 2020).
Our goal in this section is to situate online assessment within the literature of learning outcomesusing general systems theory applied to education to inform our empirical strategy in Section
3. In the conceptual framework (Figure 1), we propose that well-thought out institutionalguidelines for the online modality should be informed by inputs from missed assessments in a
feedback mechanism. System’s Theory in Education is anchored on General System Theory(GST) founded by Von Bertalanffy in the 1930s (Drac, 2015), which has been used extensively
in educational research to analyze educational output as a function of inputs at different levels(John, 2010; Garira, 2020). Systems theory acknowledges the universality of the feedback
mechanism as a necessary component to achieve desired learning outcomes. Viewed throughthis lens, institutional guidelines become part of a school’s inputs in an educational production
function in which minimizing the number of missed assessments is an objective function to beachieved for desired learning outcomes to occur (John, 2010).
We denote missed assessments as the sum of enabling and summative assessments students’failed to submit on deadline during a given semester. In reality, some students may miss only
one type of assessment, enabling or summative, and not necessarily both. Institutionalguidelines should be formulated with a clear understanding of what these rules are supposed toaddress. For example, how many missed assessments can be tolerated in online modality in agiven subject per semester? When and how should faculty intervene if there is a clear indicationthat the reason for missed assessment is not valid? These questions relate broadly to thespectrum of issues associated with formulating guidelines for effective learning outcomes.
Exhaustive discussion of factors affecting learning outcomes are diverse and complex(Malecka et al., 2020), and beyond the scope of this paper. The study’s goal is to highlight that
feedback mechanisms could be used to improve learning outcomes through integrating relevant
institutional guidelines. Figure 1 illustrates the relationships between institutional guidelines,
missed assessments and learning outcomes. When institutional guidelines are set arbitrarily,

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there is a possibility that they may create a ripple effect influencing enabling and summative
assessments, total number of missed assessments and learning outcomes
Figure 1
Proposed Relationship between Institutional Guidelines and Missed Assessments with
Feedback Mechanism

As shown in Figure 1, formative assessment does not count toward grade and is assumed toinfluence enabling assessment indirectly because many faculty members do not incentivize its
completion. It is used to prepare students for graded assessments such as enabling orsummative. Guangul et al (2020) noted that, “formative and summative assessments inconjunction with appropriate feedback systems are used to support learning in higher
education” (p. 521). Our framework (Figure 1) depicts the possible interplay between enablingand summative assessments. The double-headed arrow indicates that enabling assessment may
influence summative assessment, and can itself be influenced by summative assessment. Thisis primarily because a missed assessment is assumed to be cumulative, that is, inability tosubmit an enabling assessment may influence submission of a summative assessment and viceversa. As shown in Figure 1, the feed forward from [1] through [4] to [5] may occur in anenvironment where institutional guidelines are set arbitrarily. The dashed line depicts a system
which provides for a number of missed assessments to be used as input for policy changes at
the institutional level through a feedback mechanism. In case of well-thought out guidelines,
the possibility of [1] to [5] upper loop may be realized on efficiency grounds due to the absenceof impeding factors. Additionally, the feed forward from [1] through [4] to [5] may be
improved as well using a feedback mechanism. The proposed conceptual framework can beused to understand a system in which minimizing the number of missed assessments andimproved learning outcomes are objective functions. An empirical link between missed
assessments and internet connectivity may provide the starting point in addressing policychanges at the institutional level. We describe our data set and present summary statistics
below.

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Data Source and Summary Statistics
This study was based on the data collected from 257 undergraduate students enrolled in online
classes at a private university in the Philippines during the second semester of school year
2020-2021. The student participants were from two colleges (Education, CE and Business
Administration, CB) covering 4 programs (Education, Economics, Finance andEntrepreneurship). The two colleges were selected based on the professors’ willingness to join
the study and ability to handle the challenging data collection process. Participating facultyteach courses with term papers (with well-defined rubrics for evaluation) as part of the final
requirement. The rubrics used to evaluate the final requirement were comparable acrossprograms to reduce instructional heterogeneity.
Internet connectivity was measured through student binary answers [yes or no] to the question:
Have you missed an assessment deadline or online class due to poor internet connection? The
number of missed assessments was measured through the question: How many times in the lastsemester? The actual number of missed assessments and performance scores on the final classrequirement were generated from class records downloaded from the online platform. Students
were given at least one week to submit assessments. The online platform prevented submission
after the deadline. Other characteristics, such as personal and family background were collectedfrom students using survey questionnaire forms. Data collection was approved by the
University’s Ethics Review Committee. As expressed in the informed consent document,
participation in the survey was voluntary and included a statement regarding the right ofstudents to withdraw at any point during the data generation process without consequences.
There were no incentives given to any student for answering the questionnaire to avoid undue
influences and to minimize errors. Table 1 reports the definitions of socio-economic anddemographic characteristics of student participants.

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Table 1
Definition of Variables used in the Analysis
Variable nameDefinition
Dependent variable
Missed Assessment*
Number of missed assessments
Explanatory variables
Internet connectivity
Team participation
Performance
Age
Household size
GenderStudy hour
Father's Employed
Mother's Employed
Father's Education
Mother's Education
College of Business (CB)College of Education (CE)
Job
GPA
Team preferenceElectricity bill

Students who reported to have missed assessments due to poor internet
connection, value=1 if yes,0 otherwise
value =1 if the output is solo, 0 otherwise
Final score on paper
Age of respondent
Number of people in the household
value =1 if respondent is male, 0 otherwise
Number of hours per week in studying
value =1 if employed, 0 otherwise
value =1 if employed, 0 otherwise
value =1 if college graduate, 0 otherwise
value =1 if college graduate, 0 otherwise
value =1 if home college is Business, 0 otherwise
value =1 if home college is Education, 0 otherwise
value =1 if student has a part-time job, 0 otherwise
Current Grade Point Average (as of last semester)value =1 if student always prefer individual work not group, 0
otherwise
Estimated cost of family electricity bill per monthNote. *Summative and enabling assessments submitted after deadline

Table 2 reports descriptive statistics of students who participated in the study. The average
number of missed assessments was 2.6 (SD = 3.04), but ranged from 0 to 15. The minimum
number of graded assessments in a given semester was 12 (4 summative and 8 enabling).
Assessments were structured to ensure that one assessment did not carry too much weight in
the final grade. Formative assessments were not graded, but were commonly used by faculty
to provide students with an opportunity to practice skills as a lead-in to both enabling andsummative assessments. About 66% of students reported having poor internet connectivity,
while 34% did not report experiencing poor internet connectivity.

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Table 2
Descriptive Statistics
Variable
Poor internet connection (yes)Number of missed assessments
Gender (male)College of Business (yes)GPA
Working student (yes)Preference for group output (yes)Father is college graduate
Father has a job (yes)Mother is college graduate
Mother has a job (yes)Study hours/week
Household size
Electricity bill (monthly)
Age
Performance (Avg.score final paper)
Solo (individual output = yes)

Mean
0.66
2.60
0.31
0.79
3.35
0.19
0.67
0.63
0.79
0.68
0.59
23.88
5.21
4375.48
19.65
88.76
0.52

Std. Dev
0.47
3.04
0.46
0.41
0.64
0.67
0.46
0.48
0.40
0.46
0.49
20.52
2.53
2982.83
1.77
7.37
0.50

Min
0
0
0
0
1
0
0
0
0
0
0
1
0
600
15
70
0

Max
1
15
1
1
4
8
1
1
1
1
1
120
26
18000
35
97
1

Note. Monthly bill expressed in Philippine pesos (US$1=Php50).
Theoretical and Empirical Strategy
Formulation of Missed Assessments
Examining the link between internet connectivity and missed assessments presents special
econometric challenges. First, the number of missed assessments by students is a countvariable, therefore, treating it simply as continuous variable and applying linear regression will
result in biased estimates and may be improved using a count model such as Poisson (Greene,2003). Second, using the Poisson model does not guarantee unbiased estimates. This is because
missed assessments may not conform to the restrictive nature of the Poisson model. Greene(2003) points out, “Poisson has been criticized because of its implicit assumption that thevariance equals its mean” (p. 743). Assessments are typically cumulative, missing one
assessment increases the chance of missing another assessment as course requirements
progress over the duration of the semester. This may explain why the independence assumptionof the Poisson model is often violated (Sturman, 1999; Wooldridge, 2002).
To address this challenge, we first modeled missed assessments using the restrictive Poissonmodel to assess whether missed assessments conform to the standard Poisson’s assumption
which states that the of mean of missed assessments must equal its variance. Preliminary
analysis of the data indicated that the Poisson model did not apply. If the Poisson model wasapplicable, it would imply that missing one assessment does not necessarily increase thechances of missing another assessment. In reality, our data analysis implied that it does,
meaning that a less restrictive model like negative binomial is more appropriate for analyzing
missed assessments.

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Negative binomial is less restrictive and allows for the possibility that variance of missed
assessments can exceed the mean (Yirga et al., 2020). We modeled the number of missed
assessments of each student (yi) in the four programs during the semester using a negative
binomial model, which is assumed to take nonnegative integer values (i.e., 0 or greater thanzero). We assumed that y is a random variable which shows the number of times students havemissed assessments during the second semester, school year 2020-2021. The maximum
likelihood estimator using the Poisson distribution is used to estimate the mean. Essentially,
we are looking for the mean (lamda, λ) of missed assessments given the number of assessments(yi) missed by each student during the second semester. This problem can be said to follow the
Poisson distribution and each missed assessment has a Poisson distribution expressed as yi ~Pois (λ). The probability density function (PDF) of each missed assessment given the mean
parameter (λ) can be formally expressed in equation 1 including all relevant equations used in
the estimation process (Appendix 1). Our primary specification related the number of missed
assessments (y) to other explanatory variables (x’s) in which the key variable is internet
connectivity shown in equation 12 (Appendix 1).
Results and Discussion
We examined the link between internet connectivity and missed assessments in two ways:
differences between students reporting poor internet connectivity and those who did not reportpoor connectivity in our sample (Table 3) and incidence ratio (Table 4). Table 3 shows that themean number of missed assessments among students who reported poor internet connectivity
was 3.62 (SD = 3.12; higher than sample mean in Table 2), while among students who did notreport experiencing poor internet connectivity, which was about 0.80 (SD = 1.81) missed
assessment. The p-value is highly significant, which implies that between the two groups, thenumber of missed assessments was on average different. This first evidence provides the needfor further analysis using negative binomial in Table 4.
Table 3Comparison of students' characteristics using two sample t test
Variable
Poor Internet Connectivity
Yes
No
Mean
Std.D
Mean
Std.D
Missed Assessment
3.62
3.12
0.80
1.81
GPA
3.27
0.66
3.54
0.51
Study hours
22.53
18.84
26.60
23.40
Household size
5.18
2.22
5.27
3.07
Electricity bill4435.62951.624290.573059.73
Age
19.79
1.88
19.37
1.51
Performance
88.36
8.60
89.64
3.95
Note. ***1% ; *10%16t

-7.24 ***
3.35 ***
1.50
0.27
-0.36
-1.77 *
1.31

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Table 4Generalized binomial regression for missed assessments
Variable
Missed Assessment (Count = dependent)

IRRInternet connection(poor=yes)
AgeHome College (CBAA=yes)
Household size
Study hours
Group preference (Individual = yes)
Education of father (college=yes)
Education of mother (college=yes)
Job of mother (employed=yes)
Job of father (employed=yes)
Student (parttime job =yes)
Gender (male)Electricity bill

4.936
1.146
2.137
1.052
0.996
0.871
1.056
1.258
1.135
0.794
1.181
1.033
0.999

Constant

0.209 ***

LnalphaAlpha
LR test of alpha=0: chibar2 (01);Prob >= chibar2
Log likelihood
Number of observation
LR chi2 (12)Prob > chi2
Pseudo R2-0.4040.667
111.490.000-415.952
214
77.87
0.000
0.086

***
***
**
*

Std.
Error
0.927
0.064
0.695
0.032
0.004
0.139
0.197
0.251
0.187
0.159
0.124
0.174
0.001

Conf. Interval
(95%)
3.415
7.133
1.027
1.281
1.129
4.045
0.991
1.117
0.988
1.004
0.635
1.192
0.733
1.523
0.851
1.861
0.821
1.569
0.536
1.176
0.962
1.449
0.742
1.438
0.999
1.000

0.027

0.001

0.267

0.192
0.127

-0.779
0.458

0.084
0.972

Note. ***1%, **5% and *10%

The results of our empirical link between missed assessment and internet connectivity basedon equation 12 (Appendix 1) are presented in Table 4. But before evaluating the coefficients,
we tested the appropriateness of the negative binomial model. As discussed in the model
formulation (Appendix 1), negative binomial models assume that the conditional means are
not equal to the conditional variances. To test this assumption, we used a likelihood ratio test
that alpha equals zero. This test compares this model to a Poisson model. The lower left sectionof Table 4 shows the associated chi-squared value of 111.49 with one degree of freedom. The
probability value is highly significant, which suggests that alpha is non-zero and implies thatnegative binomial model is more appropriate than the Poisson model.
The coefficients in Table 4 are expressed as incidence rate ratios (IRR). Results indicated apositive and significant link between poor internet connectivity and missed assessments.
Students who reported to have poor internet connectivity had a 4.93 times higher incidence rateof missed assessments than students who did not report to have poor internet connectivity
holding other variables constant. On average, older students tended to have higher incidenceof missed assessments. To put it differently, a one-year increase in age tended to increase17

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missed assessments by 1.15 times (or about 15%). The incidence rate of missed assessments
for students from CB was 2.13 times higher than the incidence rate of students from CE.
Additionally, household size tended to increase the incidence rate of missed assessments.
Increase in household size by one increased the incidence of missed assessments by 1.05 times(or about 5%).
Perhaps relationships between internet connectivity and missed assessments is not surprising,
given that results from previous studies in online learning have alluded to it indirectly. In
particular, Joshi et al. (2020) showed that educational technologies are correlated with the levelof learning outcomes. Babbar and Gupta (2021) and Allen (2015) described assessments as the
key driver of quality in any educational system, and recommended requiring academic
institutions to focus on the integrity of assessments. Our results not only point attention tointernet connectivity as a barrier to online learning, but also highlight issues regarding
expectations and compliance by students with respect to online requirements. In Figure 2 andTable 5, we revisit the problem of missed assessments through visual illustration of predicted
odds given the sample of students in this study.
The findings on age indicate that missed assessments may increase with age. We found thiscounterintuitive since older students tend to be more mature and responsible. But, literature has
also shown that students of different age categories may be interested in different sets ofassessments (Aldrich et al., 2018), which may explain the differences in missed assessments.
Older students may also have additional non-school responsibilities related to work and/or
family. However, the link between age and missed assessments has not been explicitly
examined by previous studies and may require more research to understand the mechanisms
through which age affects missed assessments.
Less surprising is the incidence rate of household size and missed assessments. Thoughprevious studies may have linked household size to an array of factors including educational
goals, our interest lies on how size of household may impact the home environment setting in
online modalities, which in turn may influence students’ missed assessments. Household size
may be associated with home factors producing concurrent mechanisms with countervailing
effects. For example, an increase in household size may increase the number of people using
the internet at a given time which in turn may affect internet stability especially when
bandwidth is low.
To examine the probability and odds of missed assessments, we used information on meanvalues of missed assessments from Table 2 and a special command from Stata software toprobe further on the number of missed assessments by students. We calculated the odds ofmissed assessments by students, and used the calculated probabilities to generate Figure 2,
which visually illustrates the relationship between mean probability and number of missed
assessments in the sample. The computation of probability and odds of missed assessments inTable 5 provides useful quantitative information. For example, there is a 7% chance of not
missing assessments across all four programs in the sample, which tells us that it is possible tohave zero missed assessments in a given semester. While the zero missed assessment is not
impossible, it is not a realistic expectation for all students given what we know from Table 4.
However, looking at the other extreme, there is only about a 1% chance of missing sevenassessments in a semester. We can also examine Table 5 through cumulative probability(pcum), that is, the odds of a specific number of missed assessment. For example, the odds areabout 75% that students miss at least two but no more than four assessments, with the peak at
two (25% odds). We transformed Table 5 into a visual representation in Figure 2. In this18

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representation, the vertical axis records probability ranging from 0 to a little over 25% and thehorizontal axis records the number of missed assessments. As can be gleaned from Figure 2,
missed assessments peaked at number two. Notice how the number of missed assessments
decreases after number six missed assessments and progressively approaches zero. Figure 2
provides interesting insights regarding missed assessments and can help inform policy makers
regarding the number of missed assessments that may be deemed reasonable in any given
semester considering all possible scenarios including poor internet connectivity and other homefactors. As Figure 2 illustrates, the 7th missed assessment lies to the right and far away fromthe mean of the entire sample suggesting that the probability of accumulating the 7th missed
assessment is very low.
Table 5
Probability or Odds of Missed Assessments (2.6*)**

Number
0
1
2
3
4
5
6
7

Probability
0.074
0.193
0.251
0.217
0.141
0.073
0.031
0.011

Cumulative probability (pcum)
0.074
0.267
0.518
0.736
0.877
0.951
0.983
0.994

Note. *Mean of missed assessments; **number of missed assessments

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Mean of Probability

Figure 2
Number of Missed Assessments with Mean of Probabilities

Suppose that an institutional policy mandates the number of allowable missed assessments
during each semester to be arbitrarily set at one? This will be impractical given the underlying
forces which may increase missed assessments in a given semester. However, in order tocompel students to maintain a sense of responsibility and adhere to expectations regardless ofhome conditions, it may be reasonable to keep the number of allowable missed assessments to
between six and seven beyond which some level of penalty may be assessed in the form of
decreased score. Without clear-cut institutional guidelines, faculty are left with the burden of
having to figure out what may constitute valid reasons for missed assignments. As shown in
Figure 2, mean probabilities can help to inform institutional rules governing expectations ofstudents in online modalities.
Conclusion, Implications, and Recommendations
The unprecedented occurrence of COVID-19 pandemic compelled educational institutions tocarry out online learning modality which created challenges to school administrators, teachers,
and students particularly on the effective facilitation of the teaching-learning process. As thismethod relies heavily on internet connectivity, the current study delved into finding the link
between internet connectivity and students’ missed assessments. Results revealed that there isa positive and significant link between poor internet connectivity and missed assessments. The
findings also showed that other factors, such as age and household size are related to missed
assessments.
Our results have broader implications for the ongoing debate about how to design effective
online classes and the challenges of incorporating timely submission of assessments to aid
feedback between faculty and students, and most importantly to promote quality learning.

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While online class can never be a perfect substitute for face-to-face, institutional guidelines
must be forward looking to allow feedback and the possibility that online classes will persist
way into the future, even after the COVID-19 pandemic has ended (El Said, 2020). The analysis
we have presented here suggests that a key impediment to online learning may well beinstitutional guidelines that fail to take into account the larger picture of underlying factors
affecting students. The biggest takeaway from this research is that home environment, which
includes internet connectivity, greatly influences online learning. Finding an innovative way
to improve unstable internet connectivity is a key driver to promote both the quality andexpectations of learning. These may appear to be overstated since quality and expectations
were not directly measured in this study. In a more practical sense, the inability to summit
assessments in a timely manner, hampers the feedback mechanism that reinforces learning,which in turn may affect quality of learning.
The results offer other insights. For example, the probabilities of missed assessments calculated
in this study raises important questions regarding multiple claims of missed assessments giventhat the chances of the 7th missed assessment in a semester based on our sample is extremely
small. Our results present an opportunity for school administrators and advocates of online
learning to revisit rules governing the conduct and expectations in online modalities. In thesecond (2) section of this paper, we presented the educational context of assessments, we asked
two specific questions that faculty in any online modality may confront: how many missed
assessments can be tolerated in a given subject per semester or term?; and when and how should
faculty intervene if there is a clear indication that the reason for missed assessment is not valid?
These questions relate broadly to the spectrum of issues associated with formulating guidelines
for effective learning outcomes and deserve answers. One possible approach to answering thesequestions might involve implementing the type of framework proposed in Figure 1. Our results
provide a guide to institutional policymakers. Clearly, addressing issues regarding internet
connectivity is critical to any strategy aimed at improving learning outcomes.
Nevertheless, it is important to take note of the limitations of this study. Sample size is small,therefore both robustness and generalizability of the findings may benefit from expanding thesample size of this study. Although additional information such as dates present in online
classes, actual number of missed assessments, late submission of assessments and types ofassessments were generated from the online platform, other diverse and complex factors suchas study habits, learning attitude, digital skills, personality among others were not measured in
the survey instrument. Internet connectivity responses were based on students reported
experiences which we have no way of verifying in real time or during the period whenassessments were given. However, the methodology and model used in this study has provided
interesting insights and direction for future research. Future studies may utilize longitudinal or
bigger and more representative datasets to extend and test the robustness of findings. Quasiexperimental design may help to probe further on the relationship between internet connectivity
and missed assessments for different categories of students across schools.
Acknowledgements

We have benefitted immensely from the feedback and insightful comments of two anonymous
professors.

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what are disconnected students to do? Retrieved on September 6, 2021 fromhttps://www.brookings.edu/blog/the-avenue/2020/03/20/as-classes-move-onlineduring-covid-19-whatare-disconnected-stude nts-to-do/
Garira, E. A. (2020). A Proposed Unified Conceptual Framework for Quality of Education in
Schools. SAGE Open. Retrieved from https://doi.org/10.1177/2158244019899445
Goh, E., & Sigala, M. (2020). Integrating Information & Communication Technologies (ICT)
into classroom instruction: teaching tips for hospitality educators from a diffusion of
innovation approach. Journal of Teaching in Travel & Tourism, 20(2),156–165.
https://doi.org/10.1080/15313220.2020.1740636
Graham, A. I., Harner, C., & Marsham, S. (2021). Can assessment-specific marking criteria
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https://doi.org/10.1080/02602938.2021.1986468
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Jain, S., Lall, M., & Singh, A. (2021). Teachers’ Voices on the Impact of COVID-19 on
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Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data.
Cambridge, USA: MIT Press.
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Corresponding author: Willington Onuh
Email: willingtonoonuh@dlsud.edu.ph25

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Appendix 1
Modeling count data, starts with the most commonly used model, Poisson regression.
Following Greene (2003), the Poisson distribution with observation i can be
expressed as:

e- li liyi
Pr(Yi = yi | xi ) =, yi = 0,1, 2...
yi !

[1]

where, yi refers to missed assessment by each student in a semester, yi! is y factorial, and λi islamda which accounts for the mean incidence rate of missed assessments. The most prominentassumption of Poisson is that the conditional mean is equal to the conditional variance. The
variance-mean equality of Poisson distribution implies, var(y/x) = E(y/x). The mean parameterλi is related to (that is, a function of) regressors, xi as each yi is drawn from a sample, which isassumed to be random (i.e., independent and identically distributed). A common meanfunction, following Wooldridge (2003) and Greene (2003), is the loglinear model:
ln li = xi' b= b 0 + b1 xi1 + ... + b k xi k = b 0 + å j =1 b j xij
k[2]

since from equation 1

li = exp( x ' b ) i = 1,..., nEquation 2 models each student’s number of missed assessments as having a Poisson
distribution where the expected number (λ) is a function of regressors and the summation sign,∑!"#$ indicates the sum of missed assessments by all students. Equations 1 and 2 shows that
Poisson model is related to negative binomial and for the most part regarded as a special formof Poisson model.
Negative Binomial (Poisson-gamma) Regression Model
The main attraction of negative binomial (NB) is its flexibility allowing for the possibility forthe variance of missed assessment to be independent of the mean, which is not possible with
Poisson regression model. Thus, it allows for a scale parameter to be added in the formulationto account for overdispersion in a count data (Yehia, 2021). The NB is modelled typically as a
generalization of Poisson model by allowing for unobserved effect into the conditional mean.
This additional parameter allows the conditional variance to be greater than the conditionalmean, which accounts for overdispersion. This can be accomplished by adding an error term
to the conditional mean μ, so that the variance will be greater than the mean (Greene, 2003) as:
ln µi = xi' b + e i = ln li + ln ui ,

(ui = e i )[3]where εi a random error or unobserved variables that is typically assumed in classical regression
model, that is, error is assumed to be uncorrelated with x. The conditional distribution remains
Poisson-like in the sense that the distribution of yi conditioned on xi and ui with conditionalmean (λi) and variance (μi):

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e-liui (li ui ) yif ( yi | xi , ui ) =y!

[4]

Equation 4, is a Poisson variable with mean (λi) and error term (ui=exp(εi)) assumed to follow
a Gamma distribution. The Poisson-Gamma mixture model is assumed to have a mean of 1.0(Hilbe & Greene, 2007). The main idea of Poisson-Gamma mixture is to allow for the varianceto be greater than the mean by adding an error to the mean (λiui) or to technically account for
overdispersion inherent in count data. This means that the unconditional distribution of f (yi |xi, ui) can be derived by integrating ui out of the density (Greene, 2003; Hilbe & Greene, 2007):¥

e-liui (liui ) yif ( yi | xi ) = òg (ui )dui
y!
0

[5]

The error term, ui, defines the choice of distribution and takes the gamma type error
distribution. The g (ui) from equation 5, is a two-parameter gamma distribution (Greene, 2003),
written out as:

g (ui ) =qq
exp(-q ui )uiq -1
G(q )

[6]

where Γ(.) is a gamma function. The unconditional distribution for yi (Greene, 2003) can be
written as:¥

exp(-li ui )(li ui ) yi q q
exp(-q ui )uiq -1dui ,G( yi + 1)
G(q )0

f ( yi | xi ) = ò

yi = 0,1,...
[7]

where the y factorial, y! = Γ(yi + 1). From equation 6, the mean of gamma distribution is ɵ/ɵ
and variance ɵ/ɵ2. Constraining the mean to one implies setting ɵ = ɵ, which results in one
parameter gamma variance, where ɵ/ɵ2=1/ɵ. This expression explains why the variance is aquadratic function of the mean. The term 1/ɵ is the overdispersion parameter of Negative
binomial. The smaller the value of ɵ the higher the overdispersion allowing the mean andvariance to be different, unlike the Poisson model. The negative binomial presents a more
realistic model for estimating and understanding missed assessments by students. Missed
assessments by students are by nature events that are positively correlated by the frequency
occurrences which in turn induces larger variance. Applying properties of gamma’s integral(Greene, 2003) in equation 7, yields:f ( yi | xi ) =q q liy G(q + yi )G( yi + 1)G(q )(li + q )q + y
i

i

[8]

Using the same properties of gamma function, Hilbe & Greene (2007) provided a convenientversion of equation 8, as:qyi

G( yi + q ) æ q ö æ li öf ( yi | xi ) =
ç
÷ ç
÷ ,
G( yi + 1)G(q ) è li + q ø è li + q ø27yi = 0,1,...
[9]

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Dividing equation 9 through by ɵ yields:
q

yi

ö æ
ö
G( yi + q ) æ1
1
f ( yi | xi ) =ç÷ ç1 ÷ ,G( yi + 1)G(q ) è 1 + (li / q ) ø è 1 + (li / q ) ø

yi = 0,1,...
[10]

Redefining the dispersion parameter obtained above as α = 1/ɵ, and plugging it back to equation
10, yields a density that is commonly recognized in the literature (Cameron & Trivedi,1999;
Hilbe & Greene, 2007):
1/ a

G( yi + 1/ a ) æ 1 ö
f ( yi | xi ) =ç÷G( yi + 1)G(1/ a ) è 1 + ali ) øyiæ ali öç÷ ,
è 1 + ali ø

yi = 0,1,...
[11]

Empirical Specification of Model
To examine the effect of internet connectivity and other students’ characteristics on missed
assessments, we adopt equation 12, for estimation. Estimation of the NB model parameters(β,α) is very straight forward using software packages such as, Stata, SAS, etc. The likelihood
function can be set up from equation 11, as:l = exp( xi' b )n ìüæ a exp( xi' b ) ö æ 1 ö1öïææ 1 öï
'L(b , yi , a ) = å í yi ln ç÷ - ç ÷ ln(1 + a exp( xi b ) + ln G ç yi + ÷ - ln G( yi + 1) - ln G ç ÷ ý'aøèè a øþ
i =1 îïï
è 1 + a exp( xi b ) ø è a ø[12]Differentiating equation 12, with respect to coefficients and equating to zero yields likelihood
equations as follows:¶ log L N æ yi - exp( xi' b ) ö= åç÷ xi = 0'¶bi =1 è 1 + a exp( xi b ) ø[13]The likelihood equation of Poisson is similar to NB equation 13, but the estimates differ due
to the denominator term. However, as the parameter (α) gets closer to zero, the NB approaches
Poisson model and provides the best possible explanation why the NB is regarded as a special
form of Poisson (Hilbe & Greene, 2007).

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The Need for Digital Education in the Teaching Profession: A Path Toward
Using the European Digital Competence Framework in Albania
Heliona Miço
Epoka University, Albania
Jonida Cungu
Aleksander Xhuvani University, Albania

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Abstract
Digital competence is part of 21st Century skills that allow individual teaching professionals
to engage in new and flexible ways of learning. Studies have shown that digital competence
improves education and equips the teaching staff with expertise on how to use information,communication, and basic problem-solving. The need for digital competence is clearly evident
in the current pandemic situation, where digital technologies have taken a more prominent rolein communication and education processes. Beyond the digital competence of educators,
proper school infrastructure and curricula are needed during pre-service training to help
teachers achieve digital competence. To better understand this need, an online survey wasdeveloped to analyze digital education in Albania. The survey was designed to analyze theteaching competence of teachers in pre-service and in-service programs, as well as theirschools’ curricula and infrastructure. Results from the questionnaire highlighted a need for theacquisition of digital knowledge for teachers according to different age groups. The results ofthe study found that difficulties teachers encountered in the acquisition of digital knowledge
were not only due to deficiencies in teacher training but also other issues such as lack ofinfrastructure. The study concludes by recommending that providing digital education should
be in line with European and national policy and legislation, as well as with national andinternational organizations. The paper reports findings assessing the level of preparedness ofAlbanian educators in regard to digital education and explores opportunities and identifies
challenges for coping with enhancing digital development.
Keywords: Albania, digital competence in education, educational legislation, pre-university
education, teachers, teacher survey, students

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Society is rapidly digitalizing. Digital devices are used by preschool children and advanced
learners to perform a variety of tasks such as playing, communicating, and gathering
information. The use and application of digital devices by children has been growing, as wellas their access to digital knowledge (Caena & Redecker, 2019; OECD, 2019). The United
Nations has accepted that digital technologies have advanced rapidly than any other innovation in
the last two decades (United Nations, 2018). Digitalization has led to many technological, economic,
social and education changes in society (Gabsalamov et al., 2020; Garzón-Artacho et al., 2021).
According to the European Parliament resolution of 2021, digital transformation is shaping thelabor market, where as much as 90% of jobs are expected to require some form of digital skills.
As a result, advanced digital skills are in high demand.
In order to address digitalization, the European Parliament recommended key competences forlifelong learning. These competences highlight digital competence as one of the key
competencies necessary. Thus, it is important to focus on education as a tool to equip children,
youths and adults with digital skills and literacy. According to several studies, the digitalizationof education involves a multifaceted approach which includes replacing traditional teachingapproaches with virtual teaching and learning skills. This requires high-quality software ineducational institutions, information systems that provide access to educational resources, theintroduction of information technologies, online learning and developing learning andevaluating knowledge through digital pedagogy (Strokov, 2020; Anderson & Mattsson, 2020;
Yehya, 2021).
In their studies, Pettersson (2020) and Glover et al., (2016) emphasized the importance ofintertwining digitalization in pedagogical processes. Teaching methods that do not involve
digitalization can fail to transform practice and enhance students’ learning. This approach isalso supported by the European Digital Competence Framework (2013), which was updated in2016 (Redecker, 2017). This updated framework provides the mechanisms to understanddigital competences. In addition, it offers a variety of initiatives on European, national andregional levels meant to help young people develop digital competence. According to Ferrari(2013), the European Digital Competence Framework is an umbrella for current frameworks,
initiatives, curricula and certifications. However, teachers' digital competencies alone are notenough to digitize education. Their competencies need to be accompanied by appropriate
education policies, investments, and infrastructure. Digital transformation requires theattention of different layers of society and must be supported by several organizations(Pettersson, 2018; Babaheidari & Svensson, 2014).
Digital competencies in education, and the need for the development of information andcommunications technology (ICT) in education, are seen as necessities for the development ofpublic education in Albania (Duda & Golubeva, 2013; UNESCO, 2017; European
Commission, 2021). Despite steps taken towards the digitalization of the Albanian education
system, there is a lack of adequate teacher training and practices to ensure effective online
learning. Moreover, the COVID-19 pandemic caught the education system unprepared, withstudents unable to follow online classes due to unavailability of devices, lack of internet
connection or quality support (European Commission, 2021). The inclusion of information
technology in the Albanian education system, not just in the curriculum, needs a multifaceted
analysis. Such an analysis will evaluate the infrastructure of schools in their location. The
analysis would assess the possibility that traditional classrooms are being replaced by smart
classes equipped with audio and visual systems, schools are equipped with information systems
that provide access to educational resources, teachers have the digital competencies needed tounderstand their adaptability to digital changes, and financial resources are available. Such an

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analysis should be accompanied by the legal framework and the necessary standards
compatible with European education systems to enable its implementation by both teachersand students.
This study contributes to a portion of this larger analysis by assessing the digital competencies
of teachers, the curriculum in pre-service education and continuous educational training, andthe resources of schools in the service of digital education in Albania. An online survey wascompleted by 365 teachers of different pedagogical profiles employed in different schools
located in the city as well as in the countryside. The aim of this study was to assess the need of
educators in the area of digital education and to identify approaches toward meeting thoseneeds.
Theoritical Framework
For a teacher to serve the development of society, it is necessary to be equipped with knowledge
and skills so they can be transmitted to students. The range of competences epitomized in 21stCentury skills include critical thinking, problem solving, creativity, meta-cognition,communication, and digital and technological literacy (American Association of Colleges ofTeacher Education, 2010; Dede, 2010; Kim et al., 2019). Such skills must be understood byteachers in order to meet the demands of a global economy. In addition, new ways of learning
need to be explored for a society that is becoming increasingly mobile and digital (Council of
the European Union, 2018). For example, the European Union requires increasing attention
towards improving the level of digital competencies at all stages of education and training,across all segments of the population.
Technical knowledge is an important dimension of digital competence (Monteiro, 2015).
Aspects of teaching qualifications and professionalism, including the technical qualities ofteachers, were recognized by the 1966 International Labour Organization (ILO) UNESCO
recommendation concerning the Status of Teachers as below:
It should be recognized that advance in education depends largely on the qualifications
and ability of the teaching staff in general and on the human, pedagogical and technicalqualities of the individual teachers. [p. 79]
This position is also supported by the United Nations (UN) Special Rapporteur on the right toeducation (UN, 2016), which emphasized the need for governments to take measures toregulate online educators and develop national qualification frameworks and standards to allow
learners to receive quality education. According to Monteiro (2015), education system
performance has to be seen in the context of other systems in society, for example, health,environmental, legal, governmental, economic and technological. Hereto, the 2030 Agenda for
Sustainable Development (UN, 2015; UNESCO, 2018), adopted by the UN General Assembly,
emphasizes ICT as a means through which a range of targets will be achieved. These targets
include quality education (Goal 4); gender equality (Goal 5); infrastructure (Goal 9); reduced
inequalities within and across countries (Goal 10); peace, justice and strong institutions (Goal16); and partnerships for the goals (Goal 17). Contemporary societies are increasingly basedon information and knowledge and the comprehensive presence of technologies (UNESCO,2018). Teachers need to be equipped to guide the next generation to embrace and achieve thesegoals. The most complete acquisition of digital knowledge is done from the education system,
where teacher competences play a significant role (European Commission, 2013; König et al.,2020; Redecker, 2017).

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ICTs and “blended learning” are new paths to learning and require various skills by teachers.
Digital competence is crucial in all aspects of the teaching profession such as reflecting,researching, communicating, modelling and teaching (European Commission, 2013; GarzónArtacho et al., 2021). According to Schola Europaea (2018), digital competence involves theconfident, critical and responsive use of, and engagement with, digital technologies forlearning, at work, and for participation in society. It includes information and data literacy,
communication and collaboration, digital content creation (including programming), safety,(including digital well-being and competences relating to cyber security), and problem-solving.
The European Framework for the Digital Competence of Educators (Redecker, 2017) has
further conveyed the development of teachers' digital competences, highlighting the need for
educators to possess a set of competences. The competences have been divided into educator
professional competences, educator pedagogic competences, and learner competences(Redecker, 2017).
However, despite the fact that teacher training is a top priority for digital competencies, it isimportant to combine professional, pedagogical, technological and organizational capacities toenable these competencies to be realized (Kullaslahti et al., 2019). According to Lorente et al.
(2020), a combination of measures make digital education feasible. These measures fall into
three categories: (a) basic and computer infrastructure and equipment, as well as Internet accessin educational centers to deal with their total or partial closure; (b) the preparation and meansof teaching staff to develop teaching-learning models based on distance education; and (c) the
measures and resources implemented by countries to provide continuity for educational
processes. In addition, educator training has to be accompanied by technological infrastructure,along with software and technical support and maintenance, which require significant financial
support from the State (UN, 2016; UN & UNESCO, 2012).
Albania has made some progress promoting a knowledge-based society. This progress isreflected in the introduction of ICT in teacher education and training. Starting from 2012, there
was a legislative framework regarding the fundamental competences on education (On Preuniversity Education System in the Republic of Albania Law of 2012, Pub. L. No. 69/2012).
Digital competence is considered one of the basic competencies of pre-university students.
However, this competence does not correspond to the annual distribution of subjects at eachlevel of the pre-university education curriculum. The inclusion of information technology islimited to lower secondary education classes only (Miço et al., 2020). Nevertheless, theapplication of ICT education systems, starting from pre-school, elementary, high school,
university, and professional education is supported by Albania’s “Digital Agenda Strategy andAction Plan 2022-2026” (Albanian Council of Ministers, 2022).
As in other countries, Albania lacks legislative regulations for combining technology withimproving the teaching process, incentives for teachers to use digital competences, and thepedagogical changes needed to integrate ICT into the teaching process (European Commission,2013). In addition, school textbooks, teaching materials and the proper infrastructure,
particularly in the area of ICT, are integral parts of digital reform (UNESCO, 2017). Hence,
digital competencies are necessary for both students and teachers. Studies show that theexisting Albanian teacher competence frameworks do not acknowledge digital competences(European Education and Culture Executive Agency, 2019). Even though the use of digitaltechnology has been included in the recent undergraduate curricula at faculties of education,
further changes are needed to be implemented in pedagogy to encourage the integration of ICT
and multimedia in teaching and learning (Duda & Golubeva, 2013).

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Methodology
The researchers employed the survey method to gain insight into the level of digitalcompetence of Albanian teachers and their need to develop competence according to current
advancements in technology. As noted in the literature, the survey method is used to collectinformation from a sample of individuals through their responses to questions (Check & Schutt,2012). Further still, surveys are important in education research because they can provide
quantitative descriptions of the characteristics, behaviors, and attitudes of students, teachers,
principals, parents, district leaders, and other specific populations (Walston et al., 2017). The
study collected data through a questionnaire designed to measure the level of recognition ofdigital competence by the staff of the Albanian education system. The questionnaire wasadministered online using Google Forms. The questionnaire was organized with 18 multiple
choice questions. The answers to these questions provided information about the teaching
staff’s level of knowledge about digital competence. The questionnaire also included openended questions designed to identify difficulties encountered during online teaching, as well as
the participants’ needs for professional development in the field of digital education. The
questionnaire was completed by 365 teachers who belonged to the nine-year education cycle
and secondary education. Data collection took place during January 2022. The data collectedfrom the questionnaire served to reflect on the legal framework of Albanian education
regarding digital competence. They were also used to provide recommendations for changes
needed in order for the Albanian education system to embrace digital technology.
Participants and Their Demographics
The questionnaire was completed by 365 Albanian teachers working in pre-university
education. Participants were randomly selected from basic and secondary schools. The schools
were located both in city and rural areas. According to the demographic data collected throughthe questionnaire, participants included young and experienced teachers of different genders.
The participants held various positions within the schools.
It is important to highlight that the questionnaire was completed by teachers of a special school,
who are engaged in teaching children with special needs. Teachers of artistic Lyceum, who are
engaged in teaching musical instruments, were included as well. Consequently, thequestionnaire was filled out by teachers representing a comprehensive spectrum of Albanianteaching profiles.
The questionnaire was filled-out individually by teachers online, without any possibility ofintervention. The questionnaire ensured the preservation of confidentiality and informed
participants’ that the data of the questionnaire would be valid only for study purposes. In thesections below, questions from the questionnaire are indicated by the abbreviations Q1 for
Question 1, Q2 for Question 2, and so forth.
Demographically, the participants varied. With regard to years of experience (Q1), 19.3% of
respondents had five years of work experience or fewer, 12.1% had 5 - 10 years, 37.5% had
10-20 years, 8.8% had 20-25 years, and 22.3% had over 25 years of work experience. In termsof gender (Q2), 282 (77.3%) participants were female and 83 (22.7%) were male (Q2).
Regarding the location of the participants’ educational institutions (Q12), 133 (36.5%)
participants reported working in a city, 51 (14%) in the suburbs, and 181 (49.5%) in
administrative units and rural areas. Finally, the participants were involved in different subject

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areas (Q3), with 241 (69.5%) participants in linguistics and social sciences, 102 (29.3%) thenatural sciences, and 4 (1.2%) computer sciences.
Instrumentation
A questionnaire consisting of 18 questions, developed by the research team for this study, wasadministered to identify teachers’ level of knowledge of digital competence. In addition, thequestionnaire assessed participants’ understanding of the role digital competence plays in theteaching and learning process. There were multiple-choice questions, where participants had
the possibility of selecting more than one option and provide comments on the answer in casethey were reqiured to interpret their position. The questionnaire was sent to teachers via email
invitation. Completing the questionnaire created the possibility for each participant to be
identified by email address. Consequently, the data obtained from the questionnaire wereindividual, identifiable, and non-repetitive.
Results
Completion of the questionnaire resulted in quantitative and qualitative data, which helped
define the theoretical and explanatory framework of the study. The collected data highlighted
the digital knowledge teachers have, which differed according to age group and educational
background. The deficiencies encountered served as an indicator of the work needed tocomplete the digital competencies of teachers in the field of education. At the same time, thesedata are related to digital competences for teachers according to six separate areas of educators’
professional activities. Each question of the questionnaire contained an issue to discuss andanalyze in relation to digital education.
Facing an unusual pandemic situation forced teachers to adapt quickly to a new form oftechnology-based teaching. In the context of this immediate need, there was a lack of in-depth
information regarding the acquisition of digital knowledge by the the teachers in Albania’s
pre-university education system. Hence, the researchers addressed this issue in thequestionnaire by asking (Q4), “Did your university education have a subject on computer
knowledge?”Response to this question varied and 233 (63.8%) participants reported receiving
computer knowledge during university training while 132 (36.2%) did not. This is explainedby the fact that “computer knowledge” as a course was introduced after the 1990s curriculum.
Given that 112 (30.6%) of the participants had more than 20-25 years of work experience (Q1),
their lack of computer knowledge may be explained by the lack of this course before 1990.
Such an indicator is sufficient to create the possibility that adapting the knowledge of this groupof teachers to the new form of online learning process could be difficult and with debatableproductive effect.
The above result is supported by the Q5: “Have you acquired digital competencies during
teacher training?” For this question, participants were able to select more than one response.
As shown in Figure 1, 64 (17.6%) of participants claimed to have acquired knowledge on
digital competence during their Bachelor’s program and/or Master programs. One hundred
forty-two (39%) of participants stated they developed this competence from trainings received
from state or independent institutions. Still another 184 (50.5%) reported they developed digitalcompetence individually and another 28 (10.4%) claimed they had not been able to acquire
digital competence. Such indicators expressed the variation in teachers’ level of digitalcompetence.

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Figure 1
Source of Participants’ Digital Competence Education

Another question (Q6) sought to obtain information on the needs of teachers for additional
knowledge in the area of digital education. Specifically, the question asked, “During thelearning that you develop/have developed online, did you need additional knowledge in thefield of education digital?” As shown in Figure 2, 45 (12.4%) participants claimed to have notneeded additional knowledge in the field of digital education. In contrast, 167 (45.9%)
participants stated they needed further knowledge and 153 (41.8%) reported needing partial
knowledge. When combined, 319 (87.7%) participants reported needing to improve and gain
additional knowledge in the field of digital education.
Figure 2
Participants’ Need for Additional Digital Education Knowledge

The next quesiton (Q7) asked, “Do you think that you have consolidated knowledge in the fieldof technology in order to integrate it in your course?” Interestingly, 153 (43.8%) of participants
stated they had consolidated knowledge of technology which enabled them to integrate it withthe subject they tuaght. This is a useful result, given that the cirriculum in Albania’s pre-36

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university education is based on basic competencies. This result indicates the level oftechnology integration in pre-university education subjects. In comparison, 182 (49.9%)
participants claimed to have partial knowledge of technology enabling to infuse it into thesubject they teach. Only 23 (6.3%) participants stated they did not have to reinforce knowledge
on subject integration with technology.
The answers to Q8 provided data on the extent of development of subject competencies during
online learning: “Have you managed to develop each subject competence through the topics
covered during online learning?” Only 136 (37.3%) participants stated they managed todevelop every subject competence through the topics covered while teaching online. These
data are of particular importance as a positive indicator of the realization of basic competencies
through online learning. However, 24 (6.6%) of participants stated they were not able todevelop subject competencies and 205 (56.2%) claimed to have developed only partial
comptencies.
In order to understand how much time technology occupied when teaching in the classroom,Q9 asked: “Does the application of technology find a place during the current period thatlearning takes place physically?” For this question, 182 (50%) of participants answered
positively, 37 (10.2%) answered negatively, and 145 (39.8%) answered only partly.
Another question (Q10) aimed to evaluate the impact of teachers’ digital competence inincreasing their interest towards scientific innovations: “Has digital competence enabled you
to be in coherence with the latest scientific innovations?” The vast majority of participants(249 or 68.2%) confirmed the impact of digital competence on their interest towards scientificinnovations. Another 109 (29.9%) participants responded that it enabled them only in partwhile only 7 (1.9%) reponded negatively.
The answer to Q11, "Do you hold discussions / roundtables on specific topics for digital
education at school, for the purpose of your professional development?" provided similar
results. As shown in Figure 3, 147 (40.3%) of the participants answered yes and another 147(40.3%) answered sometimes. On the other hand, 27 (7.4%) participants stated there was a lackof the development of specific topics on digital education and 44 (12.1%) stated these topics
were often addressed.

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Figure 3
Participants’ Discussions on Digital Education

Another question (Q13) asked participants about the readiness of their schools in terms ofinfrastructure to cope with online learning. This question asked, "Is your school prepared tocope with online learning in terms of infrastructure?" In answer, 50 (13.7%) participantsconfirmed that their schools were in good condition, 221 (60.8%) indicated schools were only
partly prepared, and 93 (25.5%) participants stated that their school did not have optimal
conditions for coping with online learning.
When asked about difficulties they encountered in developing online learning (Q14), 143(39.4%) participants reported deficiencies in infrastructure and logistics. Another 79 (21.8%)
participants reported difficulties managing the technological process, 210 (57.9%) reported
difficulty meeting the needs of students, and 70 (19.3%) reported lacking relevant training in
the field of technology. Figure 4 shows the type of problems encountered by teachers during
online learning and their assessment on the level of difficulty encountered. For this question,
the participants had the opportunity to select more than one answer.
Figure 4
Difficulties Encountered During Online Learning

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Another survey question (Q15) asked about students participation in online learning. For thisquestion, 81 (22.2%) of the participants reported full participation by students during online
learning, another 243 (66.6%) reported that more then half of their class attended, 39 (10.7%)
stated that less then half of the class attended, and 2 (0.5%) stated that the students did notattend.
Participants were asked to provide reasons for their answers to the previous question (Q16). As
shown in Figure 5, 213 (59.3%) participants mentioned a lack of computer equipment, 265(73.8%) associated the lack of students’ presence to a lack of internet connection, 43 (12%)
suggested a lack of student knowledge related to connecting to digital learning platforms, and
12 (3.3%) stated other reasons without further specifying them. For this question, participants
had the opportunity to select more than one response.
Figure 5
Reasons for a Lack of Student Participation in Online Teaching

Teachers were also asked about the alternatives they would consider necessary for improving
digital competencies (Q17). For this question, 15 (4.2%) particpants indicated a need for
profound pedagogical changes, 39 (10.8%) suggested reviewing access to curricula andassessment, 17 (4.7%) indicated a need for organizational changes to the school, 138 (38.5%)
pointed to a more effective use of school facilities and teaching aids, and 152 (41.8%) indicated
all these alternatives were required at once.
In the final question (Q18), particpants were asked to comment on the difficulties encounteredin managing teaching time due to technology management. In answer, 157 (43.4%) participants
acknowledged difficulty managing teaching time. Thirty-two (8.8%) particpants reported notshowing problems in time management and 173 (47.8%) stated they had partial difficulties
with time management, mostly as a result of inefficient technology management.
Overall, the results of the questionnaire show that teachers may not be prepared for digitalcompetence. Digital knowledge is mostly obtained individually, which means that the level ofability for this individually acquired knowledge is informal and not standardized (Jong &Ferguson-Hessler, 1996).

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The majority of teachers felt the immediate need for additional training, as they were insecurein the competencies they possessed. According to Figure 2, the development of digitalcompetence in teachers is presented as an urgent need in order to achieve course integration. It
is one of the main elements for the development of teaching and learning process. Failure to
perform basic competencies due to a lack of digital skills is a critical issue, which raises
questions about the learning provided during the pandemic. Given that a high percentage ofparticipants, as evidenced in Q8, indicated that they managed to only partly cover subject
competencies, the skills students acquired during the class are concerning if subject
competencies were not fully realized.
The discontinuation of online learning and the return to physical classrooms have helped
integrate the teaching process with technology application. According to Q10, most teachers
claimed that digital competence helped them to be in coherence with the latest scientificknowledge by boosting their professional level. Professional development is a focus of teaching
staff, where the topics of digital education are among the most selected, showing a tendency ofteaching staff to improve this competence. On the other hand, the use of technology during theteaching process brings difficulties in time management.
Although the questionnaire was completed by teaching staff, 37% of which are part of schools
located in the city, only 13% of the participants claimed that their schools had optimal
conditions for the development of online learning. More than 80% of participants said that
educational institutions did not have the conditions to develop the online learning process, due
to inefficient internet connection. The results illustrated in Q13 and Q14 indicated that thegreatest difficulties during the development of online learning were encountered in meeting theneeds of students and in the deficiencies of school infrastructure and logistics (Figure 4). The
answers provided underscore that online learning did not reach a significant number ofstudents, who could not be a part of online class likely due to a lack of infrastructure (Figure5). To summarize, improving digital competence requires pedagogical change, the revision of
the curricula, and the effective use of school facilities and teaching aids.
Discussion
ICT knowledge is one of the core competences of 21st Century Skills needed for students,
educators, school reformers, college professors, employers, and others. This competence isapplicable to all academic subject areas, and in all educational, career, and civic settings
throughout a learner’s life. Yet, challenges with adapting education to technology and betterpreparing teacher education students is a long-standing issue (Falloon, 2020). To help address
this issue, the European Digital Competence Framework aims to describe digital competencies
for teachers, focusing on six separate areas of educators’ professional activities (Redecker,
2017). These areas are summarized in Table 1.

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Table 1
Study Results Corresponding to the Six Areas of Educator Professional Activities
NumberArea Description
Area 1
Professional Engagement.
Using digital technologies forcommunication, collaborationand professional development.
Area 2
Digital Resources Sourcing.
Creating and sharing digital
resources

Area 3Teaching and Learning.
Managing and orchestrating theuse of digital technologies inteaching and learning

Area 4

Assessment.
Using digital technologies andstrategies to enhance assessment.

Area 5

Empowering Learners.
Using digital technologies toenhance inclusion,
personalization and learners’
active engagement.
Facilitating Learners’ Digital
Competence.
Enabling learners to creatively
and responsibly use digitaltechnologies for information,communication, content creation,
wellbeing and problem-solving

Area 6

Study Findings
320 (87.7%) participants needed to improveand gain additional knowledge in the field ofdigital education (Figure 2)
The majority of respondents (60.8%) reported
that school infrastructure was only partiallyprepared to cope with online learning, while
25.5% reported a complete lack of optimal
school conditions for digital education (Q13)
56.2% of participants claimed that they did nothave reinforced knowledge on subject
integration with technology, or had only partial
knowledge of technology that enabled them toinfuse technology into the subject they taught(Q7).
210 (57.9%) particpants reported that one of
the most frequently encountered difficulties
during online learning was meeting the needsof students (Figure 4).
Obstacles for the active participation of
students during online learning were a lack ofcomputer equipment (59.3%) and poor internet
connection (73.8%) [Figure 5].
The teachers emphasized the need to acquire
additional knowledge in the following areas:pedagogical changes, revision of curricula andassessment, organizational changes of theschool, and the effective use of school
facilities and teaching aids (Q17).

The areas of professional skills mentioned in the European Digital Competence Framework are
related to Albania’s legal framework for education. However, although documents such as:“Professional standards of teachers,” “Competency-based curricular framework” or “Law on
pre-university education in the Republic of Albania” are outlined, it seems that the sustainable
development of digital competencies is a continuous effort to get closer to the standards of theregional and European countries. The development of digital competence for educational staff
in Albania requires an integration of technology training with teacher preparation programs(Sutton, 2011).

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The first area, which is related to professional development, describes the use of digitaltechnologies for communication and collaboration with colleagues, students, parents andothers. In addition, it emphasizes the importance of teachers’ reflection, individually andcollectively, on their teaching practices, as well as critically evaluating the effectiveness andappropriateness of their digital teaching strategies by actively developing them (Caena &Redecker, 2019). The data obtained from this study identified shortcomings in the professional
engagement of teachers in order to meet the indicator of Digital Competence of Educators(DigCompEdu). The need of teachers for additional digital competences and knowledge on
digital education, particularly during online learning, shows that the motivation to improve
digital competencies may come as a personal request of the teacher rather than as
encouragement from educational institutions.
The second area focuses on the selection and management of digital resources in education,
including knowledge on the protection and security of data disseminated in digital resources.
The Albanian legal framework does not allow educational institutions to choose or manage
digital platforms. Educational institutions do not have the right level of financal and managerial
autonomy in order to be accountable for this aspect. Since educational institutions in Albania
are not considered budgeted units in financial policies, they do not have separate budgets, and,
consequently, every educational institution is administratively dependent on local and central
government (On Pre-university Education System in the Republic of Albania Law of 2012,
Pub. L. No. 69/2012; European Commission, 2018).
Data collected for this study found that participants reported their schools were not in optimal
condition for the development of online learning. This was due to lack of infrastructure andlogistics. According to the report of the United Nations Rapporteur on the right to education,technological infrastructure, along with software, technical support, teacher training andmaintenance requires significant support from the state (UN, 2016).
Meanwhile, digital resources that make the right to education accessible do not find themselves
sufficiently in the Albanian legal provisions in force. The legal framework that includes ICTin pre-university education is incomplete. Moreover, the strategy for pre-university education
has found the use of ICT in educational institutions limited, as well as equipment that serves
this purpose, to be obsolete in most cases (Albanian Council of Ministers, 2016). According to
the UNESCO analysis of education policies in Albania, many of the 15,731 computers and
1,631 laptops in pre-university schools are not functional. Laboratory computers, in addition
to being connected to the Internet, are not equipped with curricula or subject-related content
applications (UNESCO, 2017).
The third area focuses on the management and use of technology in the learning process.
Technology can improve and develop teaching and learning strategies in a variety of ways
(Redecker, 2017). The questionnaire data showed that more than half of the participants haddifficulty integrating technology with the content of the curricula, casting doubt on theeffectiveness of teaching and learning strategies using technology. The assertion that the lackof knowledge in technology impacts teachers’ management lessons, causing difficulties, isanother indication that the development of the teaching process through technology takes
considerable time to be realized and adapted. The comparison of the survey data used in thisstudy with the information collected from the Online Learning Survey 2 conducted by the
Agency for Quality Assurance in Pre-University Education shows a deep discrepancy in thelevel of digital competence of teachers. The state survey found that 98% of teachers nationwide
were effective in linking subject topics to online teaching. In contrast, the data from this study

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revealed that 87.7% of participants needed to improve and gain additional knowledge in thefield of digital education.
The fourth area is related to assessment and addresses the concrete use of technology forassessing learning needs (Caena & Redecker, 2019). Although the information received fromteachers belongs to a period when online teaching is no longer taking place, teachers admitted
that one of the main difficulties they encountered during online teaching was related to theshortcomings in meeting student needs. Throughout the curriculum and its breakdown into
textbooks, digital education poses a fundamental challenge to the education system. Moreover,digital competence is one of the basic competencies to be integrated in the new Albaniancurricular framework. Currently, the first steps have been taken through the introduction ofdigital textbooks in the pre-university educational institutions of secondary education that havetablet laboratories for the integration of e-learning platforms (Albanian Ministry of Educationand Sports, 2016). However, the adaptation of the right to education in Albania with thedevelopment of digital competences and the effective use of ICT in school requires not only
the drafting and strengthening of the legal framework in force in pre-university education, but
above all, investments by the state to meet the needs for the comprehensive deployment of ICT,
both in infrastructure, technology, curricula and textbooks, as well as in training andqualification of the teaching staff.
The fifth area emphasizes the importance of creating learning activities and experiences that
address student needs and allows them to actively develop their learning process by helping to
improve digital competence (Caena & Redecker, 2019). Teacher statements in the survey
emphasized the lack of affecting all students during the development of online learning due tolimited computer equipment and poor internet connections.
The sixth area relates to teachers’ efforts to facilitate their students’ digital competence,
enabling them to manage risks and use digital technologies safely and responsibly (Caena &Redecker, 2019). This aspect is related to digital education that teachers should possess at
satisfactory levels. In this study, particpants expressed having an immediate need to obtain
additional knowledge in the field of digital education.
Recommendations
Preparing teachers with digital knowledge requires a great commitment from all faculties thatoffer teaching programs in education. It also requires multidisciplinary coordination withsupport from state policies in the field of education. The inclusion of digital knowledge inhigher education curricula requires the same legal support as pedagogy and psychology courses
pursued in teaching programs. The necessity of having digital knowledge for teacher training
needs to be emphasized. Moreover, it requires broad-based support from all faculties of
education that will engage in the preparation of the relevant courses. This knowledge obtainedin the faculties of education should be reinforced through continuous teacher training, keeping
teachers up-to-date with changes in digital knowledge (Falloon, 2020).
The knowledge acquired by teachers must be supported by well-organized digital systems,
which require investment by the state in hardware and software infrastructure. Investing ininfrastructure in education firstly requires the drafting of a real strategy. This will involve
making a map of the education system and understanding the relevant investments needed ineach level and school. Secondly, the investment in the school infrastructure should beaccompanied by the provision of a good digital network, extending throughout Albania.

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Moreover, the construction of suitable software for pre-university education schools is
important as it will be used to distribute each school’s information regarding student personal
information, assessments, relevant school subjects, the curriculum of each subject, tests, andthe work of students in each subject, as well as the coordination with parents or guardians forstudents’ learning progress. Thirdly, investment in school infrastructure is also related toequipping schools with computers, smartboards, and other digital devices to facilitate theteaching process. These changes will help make the teaching process intertwined with digitaltechnology to collect and transmit information in real-time.
The construction of the digital infrastructure will also ensure the integration of technology inschool curricula, interweaving the knowledge of the subject with the communication anddistribution of information to facilitate the teaching process. Teachers must be trained to use
digital software/programs and to use this knowledge in the content and development of thesubjects they teach.
The digital platforms built and implemented in schools will also be used for assessing learningneeds as well as for measuring students’ digital competence. Students today must acquire
digital competence. This will impact teaching and evaluation in order to find ways to improve
this competence. Additionally, the digital platforms should have an all-inclusive approach,enabling all users to use it, thus promoting inclusion, individual engagement, and student
involvement.
For this policy to continue, it is necessary for the establishment of a national training network
focusing on the development of teachers’ digital competence. This network will support
implementing the curriculum. The national training network can be developed and supported
by higher education institutions as a continuous qualification after initial training. This will
help build a decentralized approach from central institutions for professional development.
Following these steps will serve to implement the European Digital Competence Frameworkin equipping teachers with digital competencies.
Conclusion
The need to address digital competencies has become evident not only from international
analysis in the Albanian education sector, but was also clearly identified during the COVID19 pandemic. For a variety of reasons, Albanian educators encountered difficulties with online
teaching. This study indicates a need to strengthen digital competency in education in terms ofteaching competence, pre-service teaching programs, in-service training, curricula, andinfrastructure. The goal is a holistic approach towards a functional, productive and effective
educational system that exists within an increasingly digital society. The study highlighted thefact that the digital competence of teachers is interconnected with the digitalization of thewhole education system, and it is embedded in all aspects of the teaching profession includingreflecting, researching, communicating, modelling and teaching. Given that the Albanianeducation system continues to be centralized both politically and financially, the need forinvestment in digital education must be met by the government in parallel with curricularchanges in pre-service and in-service training. To facilitate this, mechanisms should be built
not only to promote and improve teachers’ knowledge in digital competence, but to introduce
ways of incorporating digital applications, platforms and programs that support everyday
knowledge creation and distribution. The results of this study further reveal the need to adopt
the European Framework, to include it into educational policy and legislation, and to break it
down into concrete areas of the teaching profession. The results of this study can provide a

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focal point for policy reflection on the part of the state, with the goal of increasing investment
in education to facilitate the digitalization of the system.
In addition, this study may increase awareness among faculties and universities that offer preservice teaching programs to enable different courses with a focus on improving digitalcompetence of graduating teachers about to enter future classrooms. Finally, the study seeks toprovide a voice for governmental institutions developing educational policies in Albania toprovide ongoing training for teachers in the field of digital competencies.

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OECD. (2019). Impacts of technology use on children: Exploring literature on the brain,
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United Nations. (2016). Report of the Special Rapporteur on the right to education.
A/HRC/32/37. 6 April 2016. Retrieved from https://undocs.org/A/HRC/32/37
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Corresponding author: Heliona Miço
Email: heliona.bellani@gmail.com

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Using Computational Thinking to Facilitate Language Learning: A Surveyof Students’ Strategy Use in Austrian Secondary Schools
Marina Rottenhofer
Johannes Kepler University Linz, Austria
Lisa Kuka
Johannes Kepler University Linz, Austria
Sandra LeitnerJohannes Kepler University Linz, Austria
Barbara Sabitzer
Johannes Kepler University Linz, Austria51

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Abstract
After Jeanette Wing in 2006 described computational thinking (CT) as a fundamental skill foreveryone just like reading or arithmetic, it has become a widely discussed topic all over theworld. Computational thinking is a problem-solving skill set that is used to tackle problems incomputer science. However, these skills, such as pattern recognition, decomposition,
abstraction, generalization, and algorithmic thinking, are useful in other domains, as well. This
study focuses on the use of CT skills to approach complex linguistic learning tasks in theforeign language classroom. To foster these problem-solving skills, an innovative method isused. The authors take advantage of computer science (CS) models (e.g. Unified Modeling
Language – UML) and transform them into a teaching and learning tool. This paper describes
the design and implementation of a survey used to detect students’ use of learning strategies
that are linked to computational thinking. This survey is an instrument used in a multiple-casestudy and was administered at the beginning of the interventions. The participants of the study
were learners of English and Spanish (n=66) from two secondary schools. Results indicatedthat the students were medium to low users of learning strategies that demand problem-solving
skills related to computational thinking. Differences by gender were also found, with females
reporting higher use of learning strategies than males. To conclude, the study showed a low
use of strategies among students and highlighted the importance of introducing students tolearning strategies and fostering skills needed for future professional life.
Keywords: computational thinking, digital literacy, foreign language learning, learning
strategy, modeling, visualization

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Fast technological development shapes our future and has an impact on our personal, social aswell as professional lives. For this reason, schools are confronted with high demands to equip
students with knowledge and skills that help them to cope with the challenges of the future.
According to the Future of Jobs Report 2020 (World Economic Forum, 2020), the top skills
required in 2025 are divided into four groups: problem-solving, self-management, working
with people, and technology use and development. Analytical thinking, active learning, andlearning strategies as well as complex problem-solving are at the very top of this ranking. One
problem-solving skill set, which has the potential to prepare students for future demands iscomputational thinking (CT).
Since 2006, CT has gained considerable attention as one of the core skills next to reading,writing, as well as arithmetic (Wing, 2006) and has already become part of compulsory
education in many countries, including Austria (BMBWF, 2018). With this transformation, CT
and CS models have found their way into the foreign language classroom as well. In ourmultiple case study that is based on Yin’s model (2009), diagrams from the field of computerscience (CS) are implemented as a teaching and learning strategy to foster computational
thinking in foreign language education. In computer science, on the other hand, diagrams basedon the UML (Unified Modeling Language) [Seidl et al., 2015] or Chen notation (Chen, 1976)
are used to visually depict software systems or database structures. With the use of these
diagrams in a different context as a teaching and learning strategy, the authors reach several
goals at once. Firstly, many years of implementation and research have shown that modelingwith CS diagrams is a useful visualization strategy for learners of all ages, is easy to acquire
for teachers and students, and is applicable in all subjects (Demarle-Meusel et al., 2020;
Rottenhofer et al., 2021; Sabitzer & Pasterk, 2015). Secondly, learners get in contact with arepertoire of static and dynamic CS diagrams outside computer science lessons which may help
them to familiarize themselves with this field, spark their interest, and introduce basic computer
programming concepts. Thirdly, depicting learning content with a model requires cognitive
flexibility and fosters computational thinking skills such as abstraction, generalization, patternrecognition, and algorithmic thinking. To summarize, learners do not only get in touch withcomputer science concepts but also receive a useful learning tool that they can apply in different
learning settings to solve complex tasks and memorize information long term. In the current
research, CS models are implemented as graphic organizers in several foreign language
learning settings. This paper presents the results of a survey that learners received at the
beginning of the intervention. This survey aimed to examine to what extent the participants use
learning strategies that are connected to computational thinking. For this, a survey on learning
strategies had been modified from the two German questionnaires LSN – Learning Strategy
Use (Martin & Nicolaisen, 2015) and LIST – Learning Strategies at University (Wild &Schiefele, 1994) by linking it to the areas of computational thinking.
Literature Review
In the 1980s, computational thinking (CT) was first mentioned by Papert (1980) in his workon teaching computer literacy at an early age where he saw CT as the result of his
constructionist learning theory. Twenty-six years later, the term was boosted by Jeanette Wing
as “a universally applicable attitude and skill set everyone, not just computer scientists, would
be eager to learn and use” (2006, p. 1). Since then, much research has been done and numerous
definitions emerged, many of which focus on programming, leading to the assumption that
programming is a necessary tool to teach CT (Voogt et al., 2015). However, everyone should
acquire CT, not only programmers (National Research Council, 2010) and students should get
exposed to CT long before programming (Lu & Fletcher, 2009). To date, several researchers

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have investigated the integration of CT in foreign language learning (FLL) [Barr & Stephenson,
2011; Hsu & Liang, 2021; Lu & Fletcher, 2009; Parsazadeh et al., 2021]. However, to the bestof the authors’ knowledge, none of them have investigated hands-on approaches to foster CT
in FLL in depth.
In this study, computer science (CS) models are used as a form of graphic organizer (GO) tofoster CT skills and get students engaged with computer science concepts outside the CS
lessons. GOs originally derive from Ausubel’s cognitive learning theory (1962), where heapplied them as advance organizers at the beginning of the learning process. A graphicorganizer is defined as a “visual and graphic display that depicts the relationships between
facts, terms, and or ideas within a learning task” (Hall & Strangman, 2002, p. 2). According toWillis (2007, p. 315), this creative approach “coincides with the brain’s style of patterning”
and allows students to connect the information to previously stored memories, cluster
information, discover patterns, and sort and store new data. This description is well-alignedwith CT and demonstrates the usefulness of using models to foster these problem-solving skills.
Furthermore, according to research, the use of GOs is particularly useful for students withlearning difficulties (Dexter & Hughes, 2011; Kim et al., 2004; Sousa, 2017). These results
confirm the authors’ experiences of the benefit of modeling, especially for pupils with learning
deficits. A major cause of learning difficulties in FLL such as dyslexia lies in struggles withrecognizing and using language patterns in the new language. Even if pupils suffer fromdyslexia, they may still have good intellectual abilities. However, they may not be able to noticesimilarities and differences between vocabulary and word formation patterns (i.e. semantic
processing) in the foreign language compared to their native language (Schneider & Crombie,
2012).
The difficulties in recognizing language patterns make learning difficult. However, modelingwith CS diagrams can support these pupils in their learning process. By teaching withappropriate diagrams in common FLL environments, all pupils, but especially pupils withlearning difficulties, benefit as they acquire learning content easier and thereby learn to speak
the foreign language more effectively. The following sub-section presents learning theories
connected to graphic organizers and computational thinking.
Modeling, Computational Thinking, and Theories of Learning
The use of CS models as GOs is a teaching method that combines cognitivist and constructivist
learning theories and computer science concepts to foster computational thinking skills.
Cognitivism emerged in the late 1950s and, in comparison to behaviorism that is based on thestimulus-response theory, relied on cognitive sciences by focusing on cognitive processes(Ertmer & Newby, 2013). Several cognitive learning theories support the use of GOs such as
the subsumption theory, schema theory, dual coding theory, and cognitive load theory.
According to Ausubel’s (1962) subsumption theory on meaningful learning, learning andretention are facilitated when new information is related to already existing cognitive
structures. To achieve this, he suggested the use of advance organizers. Anderson and Pearson(1988) claimed that the subsumption theory is consistent with his schema theory, where aperson has understood a text when they have found a mental “home” for the information in thetext, or else “that he or she has modified an existing mental home in order to accommodate thatnew information” (Anderson & Pearson, 1988, p. 2). The dual coding theory postulates that
there are two systems, verbal and imagery, for processing information (Clark & Paivio, 1991).
In other words, when information is presented in both forms, e.g. verbally and visually with a54

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model, chances of retrieval are increased. Lastly, the cognitive load theory by Sweller et al.
(1998) assumes that the working memory has a limited capacity and can therefore only deal
with a limited amount of information at a certain time. Used appropriately, GOs can reduce
cognitive load and lead to better learning outcomes (Rahmat, 2020).
Constructivism is often considered a branch of cognitivism. However, the main difference isthat constructivist psychologists believe “that the mind filters input from the world to produce
its own unique reality” (Ertmer & Newby, 2013, p. 55). In other words, what we know of thereal world is constructed personally with our own interpretations—“humans create meaning as
opposed to acquiring it” (Ertmer & Newby, 2013, p. 55). Out of Piaget’s constructivism, Papert
developed the learning theory constructionism, where the focus shifts “from universals to
individual learners’ conversation with their own favorite representations, artifacts, or objectsto-think with” (Ackermann, 2001, p. 4). According to Ali and Yahaya’s systematic review,
constructivist learning theory is primarily used in computational thinking focusing on primaryand secondary school levels, followed by constructionism (2020). However, they also claimthat there are many studies on CT that do not focus on learning theories at all. Bellettini et al.
postulate a social-constructivism approach to informatics and CT where the teacher’s role is to“support the construction of knowledge through setting up contexts and scaffolding materialfavoring the activation of the learning process, in which the ultimate actor is the learner itself”(2018, p. 4). This means that teachers should motivate students to use active techniques in theirlearning process.
Computational Thinking and Language LearningThis section describes the core elements of computational thinking that are the focus of thecurrent study. In the literature, CT is represented with different manifestations, core concepts,
and skills. The Joint Research Center (JRC) from the European Commission (Bocconi et al.,2016) conducted a literature review and analyzed the skills emerging from the most prominent
papers on CT. As a result, they developed a list of core elements, which are abstraction,
algorithmic thinking, automation, decomposition, debugging, and generalization. In this study,
the authors refer to the elements proposed by the JRC, extend them with pattern recognition(Curzon et al., 2019), and link them to foreign language teaching. Additionally, this section
gives best practice examples on how to use modeling and CT as techniques that support
students in creating new knowledge and engaging them actively in the learning process.
Decomposition
Decomposition is the process of dividing a bigger problem into smaller sub-problems (Barr &Stephenson, 2011). This divide-and-conquer strategy helps to facilitate the understanding of aproblem and, thus, can be solved systematically as well as individually. In language education,
this is a skill widely used. For example, when writing a paper only a few people would write itstraight from the beginning to the end. Usually, the structure of it is well thought-through andheadlines like “introduction”, “methodology”, “conclusion”, and so forth. are created first.
Then, additional arguments or topics are found for the main body. The introduction andconclusion are also known to be written last. This process illustrates decomposition at its best.
Abstraction
Abstraction describes the process of reducing complexity by omitting unnecessary details.
Thus, the main characteristics of a problem or item are defined. Everyone handles abstract55

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objects daily, for example, when using a map. Every map is a simplified presentation of reality.
When learning about giving directions in the language classroom, subway maps are a common
tool taken from real life. Another example is writing a summary. A summary is characterized
by leaving out unnecessary details and concentrating on the most important information.
Hence, training on writing summaries and encouraging students to take notes or highlightimportant information in a text, also helps to strengthen computational thinking skills. In
computer science, class and object diagrams are used to visualize various components of asystem and their relations (Seidl et al., 2015). Whereas class diagrams describe the abstract
model of a system (e.g. animal), object diagrams illustrate concrete objects (e.g. cats and dogs).
In the language classroom, these models can be used to develop new vocabulary about specifictopics, illustrate relations and hierarchies, and categorize these items. Figure 1 shows a simple
example of one class. As can be seen, the name of a class is always a noun, attributes are seen
as adjectives, and methods as verbs. Thus, students can also practice the difference between
these word classes and word formation.
Figure 1
Class Diagram

Another model, which is used in computer science frequently, is the entity-relationship model(ER model) [Chen, 1976]. It consists of three elements – rectangles as “entity-types” that areused as nouns, diamond shapes as “relationship types”, and the ellipses as “attributes” that
describe the characteristics of the nouns. The ER model can be used as an intermediate stepwhen writing summaries, supporting especially students with learning difficulties when writing
texts. Figure 2 shows a model where elements of a text on COVID-19 were transformed into
an ER diagram with concrete and generalized terms. Usually, in computer science, the ER
diagram only uses generic terms instead of specific terms since it represents a type of a system
and not an instance (Bagui & Earp, 2003). However, in the language classroom, this can beadapted by using concrete terms of a text and/or abstract terms.

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Figure 2
Entity-Relationship Diagram

Pattern Recognition and Generalization
Finding patterns is something inherently human, and the brain can remember patterns moreeasily (Grabmeier, 2018). As soon as patterns, similarities, and connections are found, ageneralization of these can be done, and already known problem-solving strategies which
worked for a similar scenario can be re-used. Also, in many cases, it is possible to draw
conclusions from a part or general to the whole. Every language educator who used an
inductive method is already familiar with pattern recognition and generalization. For example,
the teacher provides various grammatical items such as sentences in the past tense using regularverbs. Subsequently, the students have to find grammatical rules based on the examples given.
Figure 3 illustrates how the use of an activity diagram can visualize the grammatical rules, suchas the use of “for” and “since” in English. Also, it can function as a step-by-step guide.
Figure 3
Activity Diagram showing the Use of For and Since in English

Another example in which generalization in the language classroom is used is by giving
examples and prompts in which generalized terms like genre, title, author, and so on, are used.

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The students then have to find the actual genre, title, and author of the presented text, that is,gothic novel, Frankenstein, and Mary Shelley.
Algorithmic Thinking
An algorithm is often described as a step-by-step guide comparable to a recipe. Teaching
students to write good recipes can be compared to writing an algorithm. Not only is it important
to be precise in its formulation, but also to think systematically about which step comes after
the other. How long do you have to beat the eggs to make your cake heavenly fluffy? Usually,teachers give the exercise to simply write a recipe, but for students with learning difficulties, it
may be a good idea to sketch the information at first via an activity diagram. With thisintermediate step, they not only have the structure first but also the key vocabulary needed forthe exercise. Figure 4 shows an example of an activity diagram created for a recipe.
Figure 4
Algorithm for Making Tea

Testing and Debugging
It is not enough to find solutions for problems; it is also necessary to systematically analyze
these solutions using skills such as testing, tracing, as well as reasoning. Based on this accurate
analysis, errors can be fixed and results predicted and verified. In the language classroom,
students can be trained to achieve this by correcting (one’s own) errors, for example, in a filling
the gap exercise or when learning how to give feedback.
Automation
Automation is a work-saving process in which a machine or computer is instructed to perform
a series of repetitive tasks quickly and efficiently compared to the processing power of ahuman. This is the only skill that usually is not very common in the language classroom,58

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although there would be possibilities to include programming as well, for example, with theuse of the programming language Scratch or exercises from machinelearningforkids.com.
Methods
BackgroundIn the school year 2020/21, a multiple case study (Yin, 2009) on modeling as a teaching andlearning strategy to foster computational thinking was conducted. The subjects of the casestudies were partner schools of the COOL (computer sciences-supported, cross-curricular, andcooperative open learning) Lab at the Johannes Kepler University in Linz. The JKU COOL
Lab is an innovative teaching and learning lab for teachers, children of all ages, and university
students. It focuses on computer science, computational thinking, and digital literacy. The lab
has many offerings including workshops, weekly clubs for gifted students, theater shows on
digital education, teacher training, and so forth. In addition to offerings for all interested parties,
the lab works intensively with several partner schools where projects are implemented andresearched over a longer period. In the multiple case study, modeling was implemented in four
foreign language classes of two partner schools to find out more about (1) teachers’ andstudents’ perceptions of modeling as a teaching and learning strategy, (2) the chances andchallenges of the implementation of modeling and (3) computational thinking as a problemsolving strategy. This paper focuses on computational thinking as a problem-solving strategy
and presents the results of a survey administered to all the participants of the multiple casestudy at the beginning of each of the interventions. This survey aimed to find out more about
students’ use of learning strategies that are related to computational thinking. In particular, thefollowing research questions were explored:
1. Is there a connection between learning strategies and the areas of computational
thinking as a problem-solving strategy?
a. If yes, what strategies are associated with computational thinking?
2. Do students use strategies associated with computational thinking to better understandand process learning content?
3. Does the use of learning strategies differ by gender?
Participants
The questionnaire was administered to a total of 66 students (nf = 31, nm = 35) from two partner
schools (PSn) of the JKU COOL Lab. In those partner schools, several teachers collaborated
intensively with the researchers and two of them were willing to participate in this study. Thus,
random sampling was not possible. Before conducting the study, written permission was
obtained from the school principals as well as the parents of the participants. Both groups ofPS1 (English class) and PS2 (Spanish class) were involved in the multiple case study forseveral months working with models as a teaching and learning strategy to foster computational
thinking skills. To get an insight into students’ computational thinking strategy use, the survey
was administered at the beginning of the intervention. In the English group composed of 51
students, there were 29 males and 22 females with a mean age of 14.25 and a standard
derivation of 1.369. The Spanish group consisted of 15 students, 6 males, and 9 females witha mean age of 13.27 and a standard deviation of 1.981. At the beginning of the study, none of
the students were familiar with modeling and the concept of computational thinking. The
demographic information of the participants is presented in Table 1.

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Table 1
Participants in the Study
School Subject

N

Male Female

PS1
PS2

51
15

29
6

EnglishSpanish

22
9

Mean
Age
14.25
13.27

SD
1.369
1.981

Instrument
In this study, a paper-based questionnaire on learning strategies was administered, consistingof 37 Likert-formatted items. For this survey, the authors adopted items from the LSN(Learning Strategy Use) questionnaire from Martin and Nicolaisen (2015) and combined itwith four items from the LIST (Learning Strategies at University) questionnaire (Wild &Schiefele, 1994) bringing it up to 37 items.
The four LIST items were the following:
1.
2.
3.
4.

I try to organize the material so that I can easily remember it.
I visualize the material to be learned.
I learn key terms by heart to help me remember important areas of content.
I memorize a self-made overview with the most important terms.

The frequency was measured with a five-point Likert scale ranging from 1 (very rarely) to 5
(very often). The questionnaire was issued in German and was translated for this paper.
Procedure
The questionnaire was administered to the students at the beginning of the multiple case study
in their regular language lessons. The participants of the study had no previous knowledge ofmodeling and computational thinking. The survey had no time limit to make sure the students
were not under any pressure and could think deeply about their answers. The students needed
approximately 10-15 minutes to respond to all the items of both Part 1 and Part 2 of thequestionnaire.
To identify which learning strategies are used that relate to CT skills and visualization, threeexperts independently analyzed the first part of the questionnaire and filtered out the items (137) that can be assigned to the CT skills mentioned in section 2 on the one hand and tovisualization strategies on the other. After this analysis, the experts discussed the respective
selection and decided on the items used and their assignment to the respective categories. The
statistical analysis was then conducted using the statistical software IBM SPSS Statistics 23.
Findings
Learning Strategies Related to Computational Thinking
The first research question sought to answer whether there is a connection between learning
strategies and the core elements of computational thinking and if yes, which ones. The expert
analysis has shown that a total of 22 Likert items can be related to computational thinking andvisualization strategies. Specifically, 18 items from the Martin and Nicolaisen questionnaire

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(2015) relate to computational thinking, and two items each of the LIST relate to visualization
and CT (Wild & Schiefele, 1994). Table 2 shows an overview of the remaining items and theallocation to the individual areas. Since all the CT skills are intertwined, some items havemultiple assignments. From the core CT skills proposed in section 2, all the skills except“automation” could be associated with items in the questionnaire.
Use of Strategies Related to Computational Thinking
The second research question investigated whether students use strategies associated withcomputational thinking to better understand and process learning content. To find out which of
the CT skills according to the learning strategies are used the most, descriptive statistics,
including means and standard deviation of the six CT categories as well as the category related
to visualization was used. As illustrated in Table 3, three categories are above the middle of
the Likert scale and four are below it. Testing and debugging strategies are used mostfrequently (M= 3.28; SD= .76), closely followed by decomposition strategies (M= 3.10; SD=.78) and algorithmic thinking (M= 3.03; SD= .79). On average, the categories below the mid
point are: generalization (M= 2.88; SD= .90), abstraction (M= 2.83; SD=.89), patternrecognition (M=2.78; SD= .87) and lastly, visualization (M= 2.39, SD=1.09). According toTable 3, all categories had a mean score at the medium or low level. None of the categories
had a mean value at a high level above 4.0.
Besides the descriptive analysis of the seven categories mentioned above, individual items
were also ranked and highlighted as the five most and least commonly used learning strategies.
As seen in Table 4, the most common strategy is to use the internet or dictionary when words
are unclear (M=3.91; SD=1.32), whereas the least common strategy (see Table 5) is to create
drawings or sketches to better see how things belong together (M=1.95; SD=1.07). Looking at
all 22 items, none of the items has a mean value at a high level above 4.0. Half of the items(N=11) have a mean score at the medium level above 3.0, whereas 10 items are above 2.0 andonly one item below.

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Table 2
Survey Part 1: Likert-Items
Nr.

Item

Category

1

When I have to study for an exam, I make a short summary.

AB

2

I often do drawings or sketches to better see how things belong together.

V

3

I underline the important passages in the textbook.

AB

4

I try to organize the material so that I can easily remember it.

AL5

I visualize the material to be learned.

V

6

When I learn something new, I try to figure out what to do with thatGE

knowledge (what is the practical use?).
7

I wonder how what I am learning relates to what I have known so far.

AB

8

I wonder if what I am learning or hearing is logical.

TD

9

I wonder if there could be other explanations for what I read or hear.

GE, TD, PR

10

Instead of studying for a long time, I spread the work over several days.

DC11

I repeat things (such as foreign language vocabulary) in small portions, but

DC, AL, PR

regularly (e.g. every day for 10-15 min).
12

I learn key terms by heart to help me remember important areas of content.

AB, GE

13

I memorize a self-made overview with the most important terms.

AB, GE

14

When my learning is not going well, I try to change something and see if it

TDgoes better.
15

Before I start to work, I set myself clear goals.

DC

16

While studying, I check whether I am still on the right track.

TD

17

When I stop working, I check whether I have achieved my goals.

TD

18

When I study, I make a realistic schedule.

AL, DC19

I make sure that I have enough time the day before an exam to review all ofDCthe material again.
20

Before an exam or a lecture, I think about what to do if things do not go well.

AL

21

I look for more information in books or on the Internet if something is notTDquite clear to me.
22

If I do not understand words, I look them up on the Internet or in a dictionary.

TD

Abbreviations: DC Decomposition, PR Pattern Recognition, AB Abstraction, AL Algorithmic Thinking, GE
Generalization, TD Testing & Debugging, V Visualization

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Table 3
Descriptive Statistics – Computational Thinking Skills. N= 66
Minimum

Maximum

Mean

Std. Deviation

Decomposition

1.40

5.00

3.1010.77854

Pattern Recognition

1.00

4.67

2.7778.86791

Abstraction

1.00

4.75

2.8258.89224

Algorithmic Thinking

1.25

5.00

3.0253.79124Generalization

1.00

4.50

2.8750.90219Testing & Debugging

1.29

4.86

3.2835.75696Visualization

1.00

4.50

2.39391.09374

Table 4
Top 5 of the Most Commonly Used Learning Strategies. N= 66
Nr.
1. If I do not understand words, I look them up
on the Internet or in a dictionary.
2. I wonder if what I am learning or hearing is
logical.
3. I make sure that I have enough time the day
before an exam to review all of the material
again.
4. I try to organize the material so that I caneasily remember it.
5. Before I start to work, I set myself clear goals.

Category Min.

Max.

Mean

Std.
Dev.

22

TD

1

5

3.91

1.321

8

TD

1

5

3.73

1.089

19

AL

1

5

3.46

1.251

4

AL

1

5

3.41

1.265

15

DC

1

5

3.38

1.034

Abbreviations: DC Decomposition, AL Algorithmic Thinking, TD Testing & Debugging

Table 5Top 5 of the Least Commonly Used Learning Strategies. N= 66
Nr.

Category

Min.

Max.

Mean

Std.
Dev.

2

V

1

5

1.95

1.073

1

AB

1

5

2.52

1.099

3. When I study, I make a realistic schedule.

18

AL, DC

1

5

2.61

1.341

4. I wonder how what I am learning relates towhat I have known so far.

7

AB

1

5

2.65

1.295

5. While studying, I check whether I am stillon the right track.

16

TD

1

5

2.73

1.103

1. I often do drawings or sketches to bettersee how things belong together.
2. When I have to study for an exam, I make ashort summary.

Abbreviations: DC Decomposition, AB Abstraction, AL Algorithmic Thinking, TD Testing & Debugging, V
Visualization

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Learning Strategies, Computational Thinking, and Gender
The last research question sought to answer whether strategy use related to CT differs bygender. The independent Sample T-Test revealed that female students reported statisticallymore frequent use of learning strategies related to CT than male students did. Female students
have a higher mean score in relation to all learning strategies (Mf = 3.31; SDf = .58, Mm= 2.80;
SDm= .63, p<.05) as well as in the different CT categories (see Figure 5). However, whenlooking at the single CT categories, only decomposition, abstraction, generalization, andtesting and debugging were found to be statistically different (P<.05).
Figure 5
Mean Score of CT Strategy Use Related to Gender. N=66

A Pearson correlation coefficient was computed to assess the linear relationship between
gender and CT categories, as well as overall strategy use. As reported in Table 6, there is astatistically positive correlation between gender (1=male, 2=female) and decomposition,
abstraction, generalization, and testing and debugging as well as the overall strategy use.
Table 6
Pearson Correlation Coefficient between Gender and Strategy Use. N= 66

genderPearsonCorrelationSig.
(2-tailed)N

gender
1

66

DC
.254*

PR
AB
.219 .357**

AT
.211

GE
.259*

TD
.384**

V
SUM.176 .393**

.039

.077

.003

.088

.036

.001

.158

.001

66

66

66

66

66

66

66

66

*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

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Discussion
This research sought to investigate the connection between learning strategies and the areas ofcomputational thinking as well as students’ use of the respective strategies. The results of thesurvey indicate that the participants were medium to low users of learning strategies thatdemand problem-solving skills related to computational thinking. These findings are consistent
with previous studies on learning strategy use. For example, Aslan (2009) investigatedlanguage learning strategies and also found a medium level of strategy use regardless of gender.
However, he found that higher-achieving students use more learning strategies. The low use oflearning strategies, in general, may have several reasons. One explanation may be that students
do not know which strategies are effective (Morehead et al., 2016). Korenell and Bjork (2007),on the other hand, found that many students’ goal is to pass exams and not to store information
long-term. Another possible explanation for the rare use of learning strategies could be the time
factor. Previous studies have shown that many students do not split learning content over alonger period, but rather wait until just before an exam, often until the last day (Blasiman etal., 2017; Susser & McCabe, 2013; Taraban et al., 1999). There are similarities between theattitudes expressed by the participants of this study and those mentioned above. It is apparent
from Tables 4 and 5 that many students make sure to have enough time the day before the exam
(Item 19) and pay less attention to making a realistic schedule (Item 18) or checking whether
they are still on the right track (Item 16). Hence, it could be hypothesized that the lack of timeis the reason why students prefer quick searches for information (Item 22) rather than timeconsuming strategies (Items 1, 2). Time constraints could also be the reason why students areless concerned about linking new information to prior knowledge (Item 7). However, it seems
that students still organize their work, try to set goals (Items 4, 15), and question the new
information (Item 8).
The results also demonstrate a statistically significant difference in learning strategy use bygender and correlate well with previous studies in the context of language learning wherefemales surpassed males. In his work on language learning, Oxford reports on females “using
more varied strategy types and employing strategies more frequently than males” (1993, p. 85).
Furthermore, he claims that when students are not explicitly asked by the teacher to use acertain L2 learning strategy, they tend to use those favoring their learning style. For example,
analytic learners (often males) prefer strategies involving logic, whereas the global learner(often females) prefer to use social strategies including searching for the main idea andintuitively guessing. In a study on gender and language learning strategies in learning English,
Aslan (2009) also found a significant difference in strategy use, indicating that females, on
average, employed more strategies than males in all domains and subscales investigated.
Although a great amount of literature reports a significant gender difference proposing thatfemales generally use more learning strategies than males, few studies came to the opposite
conclusion. For instance, Tercanlioglu (2004) conducted a study on foreign language learning
strategies with 184 pre-service teachers from Turkey, showing a gender difference favoringmales. According to her, the cultural background could be one of the reasons that the results
are not consistent with many previous studies.
Limitations
This survey helped to illuminate strategy use of students and served as the basis for the
implementation of CS modeling in foreign language learning to foster computational thinking
skills. Nevertheless, the study also has its limitations. One of them includes the self-selection

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bias resulting from the collaboration with the partner schools of the JKU COOL Lab. Anotherlimitation of this study is the sample size. Further research and wider trials are needed to beable to generalize the results and to determine which other factors besides gender influencestrategy use. Moreover, to be able to fully understand this phenomenon, the use of further datagathering instruments such as interviews is also advisable, so that the case can be viewed fromdifferent angles leading to richer results and conclusions. A major reason why only thequestionnaire was used at the time of the study was due to the difficult circumstances causedby the COVID-19 pandemic. Therefore, further investigations with interviews are planned toget a more holistic picture.
Recommendations and Conclusion
This survey aimed to investigate the use of learning strategies that can be linked to the coreelements of computational thinking (CT). For this, an expert group analyzed and identified
items of the two German questionnaires LSN (Learning Strategy Use) [Martin & Nicolaisen,
2015] and LIST (Learning Strategies at University) [Wild & Schiefele, 1994], and developed
a list of learning strategies related to computational thinking. By analyzing the degree ofstrategy use among students, this study established that all participants in the study were only
medium to low degree strategy users. Furthermore, results show that females reported
statistically higher use of learning strategies related to CT than male students. When looking
at the six CT skills as well as visualization strategies, testing and debugging strategies marked
the highest usage, closely followed by decomposition strategies and algorithmic thinking. The
category of visualization skills occupied the last place in the ranking. Concerning individual
strategies, item 22 (If I do not understand words, I look them up on the Internet or in adictionary) was the most frequently used strategy, and item 2 (I often do drawings or sketches
to better see how things belong together) was the least frequently used strategy.
These results indicate that although students are generally medium to low users of strategies,
they prefer fast strategies like researching information online to techniques that are more timeconsuming, such as visualization strategies. It is also possible that students are not aware of theeffectiveness of various strategies, especially for retaining information long-term. CT skills
such as decomposition, abstraction, pattern recognition, and algorithmic thinking are essential
for future professional life. Thus, an important implication is that teachers should raise strategy
awareness and offer students opportunities to gain these skills by providing suitable activities
such as modeling. With this approach, students’ interest in more time-consuming visualization
strategies can be increased as they might see long-term benefits that outweigh expenditure oftime.
The results of this survey work as the basis for the implementation of computer science models
as a teaching and learning strategy to foster CT skills. The experience and research on modelingand CT in language teaching and other subjects have shown promising results in recent years.
Nevertheless, future work is planned to investigate the reasons behind the low use of strategies
generally and visualization techniques in particular. Moreover, further studies could shed more
light on the contribution of higher CT strategy use on learning achievement. To conclude, withmodeling as an innovative teaching and learning strategy and other appropriate activities, theauthors hope to foster students’ CT skills, reduce cognitive load, and promote strategy use andsustainable learning.

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Corresponding author: Mag. Marina Rottenhofer
Email: marina.rottenhofer@jku.at

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A Comprehensive Community of Inquiry Framework for Exploring
Technology Enhanced Language Learning
Irum Alvi
Rajasthan Technical University, Kota, India

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Abstract
The use of technology for second language (L2) acquisition has become ubiquitous, but little
thought has been given to the factors that impact the language learning experience. This study
aims to use the Community of Inquiry (CoI) framework to propose and validate a morecomprehensive model for investigating the influence of presence on learners’ L2 learning
experience using Technology Enhanced Language Learning (TELL). Data were collected froma sample of language learners (n = 129) studying in the State of Rajasthan, India using an equestionnaire. To scrutinize the effect of various forms of presence, descriptive and inferential
analyses were conducted. The findings suggest strong, positive, and statistically significant
associations exist between the original CoI elements (teaching presence, cognitive presence
and social presence), the newly added elements (learning presence, emotional presence, andtechnological presence), and learning experience. These results confirm the idea that presence
can hinder and/or enhance L2 learning experiences. No association was found between
technical barriers and learning experience. The findings have theoretical and practical
implications. The results suggest the value of expanding the CoI framework, scrutinizing thelearners’ experience, analyzing the influence of presence, and enriching the application of thetechnology for language learning. Such results may ensure TELL courses are designed as
vigorous learning environs which facilitate language acquisition.
Keywords: community of inquiry, learning experience, teaching, cognitive and social presence,learning, emotional and technological presence

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The COVID-19 pandemic had a significant influence on the learning experiences of learners(Yu et al., 2022). During this turbulent time, Technology Enhanced Language Learning (TELL)
became the only means of learning. TELL refers to a teaching methodology, which requires
electronic resources (e-resources) for promoting teaching and learning of a second language(L2). TELL deals with the manner in which technology affects the instruction and acquisitionof a L2. Technology has been useful in enhancing input quality, authenticating communication,and giving quick and pertinent feedback, for the development of all language abilities,
including listening, writing, reading, speaking, grammar, and vocabulary. The adoption oftechnology for learning assists learners in acquiring new knowledge and skills (Wood &Shirazi, 2020); it also opens new vistas for research (Rasheed et al., 2020). Instead of the mostfrequent language learning approaches used in higher education (e.g., face-to-face,
blended/hybrid, flipped), TELL has emerged as one of the most important means of language
learning in India. This change has been accompanied with a shift from teacher-centered tolearner-centered approaches to learning, as learners become involuntarily dependent ontechnology (Dhawan, 2020) as part of the new normal (Zhou et al., 2022). Previous researchlooked into how learners accepted diverse forms of technologies including social networking
tools (Alvi, 2021a) such as WhatsApp (Kaur et al, 2021), learning management systems(Camilleri & Camilleri, 2021), artificial intelligence for integrated learning (Mageira et al.,2022), and feedback (Alvi, 2021b). Based on these studies, learners’ perceptions andacceptances emerged as central factors in shaping their learning experience (LE), in atechnology-supported learning environment (Huang & Liaw, 2018).
Peirce and Dewey introduced the concept of community of inquiry (CoI) and linked it to theinquiry stage of knowledge acquisition describing the essential elements of a successful
learning experience (Garrison, 2017) in higher education. Several researchers have used the
CoI model based on social constructivism theory to examine LE (Junus et al., 2022; Yu & Li,2022). The CoI model is broadly defined as a learning procedure or setting where learners learn
together in an environment where they can make enquiries to overcome complicated problems
or learn new information. According to social constructivism, all knowledge is created throughlanguage use and social interaction, making it a shared rather than a private experience. Social
constructivism explains how people learn and gain information. Moreover, the goal of an
educational community is to create a setting where learners participate together to better grasp
or experience the required skills.
The current study uses the CoI framework (Garrison et al., 2000) to investigate factors
influencing learning English as a L2. CoI offers a conceptual model for e-learning experiences.
The original model consists of three main constructs: social presence (SP), teaching presence(TP), and cognitive presence (CP) (Kozan & Caskurlu, 2018). Initially, utilized to investigate
how e-forums produce intellectual and social community, the CoI framework has beensuccessfully used for e-learning/blended learning (Kilis & Yıldırım, 2018). Despite its
strengths, numerous studies suggest the CoI framework can be enhanced by adding newpresences to account for important factors of effective e-learning (Cleveland-Innes &Campbell, 2012; Kozan & Caskurlu, 2018). The present study posits that the addition of newpresences, namely learning presence, emotional presence, and technological presence to the
CoI framework, may provide more insight into how students perceive their learning
experiences in L2 classrooms. Because research on the CoI framework and its application
remain relatively recent, there are no in-depth studies investigating them in the context ofTELL, particularly in the Indian context. To address this gap in research, this study aims toinvestigate the influence of presence on learners’ L2 learning experience using TELL. The

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study addresses two research questions: 1) What factors impact the TELL learning experiencein India? And 2) What level of presence influences the TELL experience?
The main objective of this research is to look into learners’ LE with two main objectives. The
main goal is to create a comprehensive and inclusive version of the CoI framework to better
understand the factors impacting LE in the context of TELL in India. The second goal is toexperimentally and analytically validate the proposed CoI based framework. As such, thepresent study extends and validates the modified model using raw data and empirical tests inthe context of TELL. As illustrated in Figure 1, the study modifies and extends the CoI model
for assessing the TELL of language learners in India. In short, the study’s rationale is to
scrutinize language learners’ LE and to offer an all-inclusive framework for investigating andenhancing their experiences. The rest of the paper discusses of the CoI framework, in thecontext of L2, which provides a solid, descriptive theoretical foundation. Then, it proposes a
modified model grounded on the existing CoI model, which investigates the critical elements
for an effective e-learning experience based on social constructivism (Dewey, 1959). Finally,
it validates the model using data collected from language learners.
Figure 1
Conceptual Model Based on an Extended CoI Framework

Literature Review and Hypotheses Development
Language learning is closely connected to language use. It develops through social interaction,and is a social process which requires sustained communication. TELL provides learners withthe opportunity to engage in several ways using diverse online language learning tools. The
CoI framework, which includes three elements called presences, has been successfully used
for investigating the use and implementation of technology for L2 learning (Wang et al., 2022).
The term presence refers to a social situation that arises from interactions between students andteachers (Picciano, 2017). This feeling of presence is needed for enhancing language learners’
performance and language acquisition. Interactive environments enabled by modern
technologies are beneficial for L2 learning. Within an epistemic engagement pedagogicalapproach, the CoI model may be regarded as an exemplary model to understand e-learning

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(Shea & Bidjerano, 2010). This is particularly true for integrated, team-based e-learning(Parrish et al., 2021). The CoI model captures not only the most important aspects of learning(teaching, cognitive, and social), but also the dynamics of the e-learning environment (Garrison
et al., 2010). As such, many academics have endorsed the CoI framework (Wang et al., 2022),
while others have pointed out its limitations by criticizing it for lacking representation ofdiverse educational areas (Annand, 2019). Prior research has also called for more empiricalresearch to test the efficacy, scalability, and feasibility of CoI in different contexts (Annand,2019). As such, the current study aims to fill this research gap.
Cognitive Presence
Cognitive presence (CP) denotes the ability of learners to confirm/construct significance basedon continued consideration and reflection (Garrison et al., 2001). Researchers haveconcentrated extensively on socio-cognitive assessments of e-learning for collaborativeinteraction (Park & Shea 2020). Much of this research has focused on CP, considering it asignificant measure of the quality of learner experience as it entails genuine methodologies
grounded on constructing understanding in an e-environment (Garrison et al., 2017). CP
consists of four phases: triggering events, resolution, exploration, and integration. Triggering
events refer to tasks, inquiries, or stimuli, which encourage a sense of mistrust, wonder,
bewilderment, and uncertainty in learners. Such experiences enhance the need for inquiry
because it motivates the learner to address their cognitive conflict. Triggering events encouragelearners to use their inductive reasoning to overcome the lack of existing awareness andcomprehend any new information, which leads to resolution. The third phase called explorationrefers to learners’ efforts to overcome their cognitive dissonance by reconstructing knowledge
and by searching for new information. In these phases, learners discuss facts and information
among themselves, share ideas and recommendations, and prior experiences, and investigate
concepts. Integration is the final phase wherein learners connect the material gathered in theearlier stages to reach tentative solutions or reasons. This stage is crucial for learners to build
higher-order thinking skills (Garrison & Arbaugh, 2007). Based on the extant literature on CP,
it can be argued that it has a significant effect on the learners. Thus, the first hypothesis for thisstudy (H1) stated that there is a statistically significant positive relationship between CP and
LE among language learners.
Teaching Presence
Teaching presence (TP) refers to the design, expedition, and direction of social and cognitive
process for realizing meaningful academic learning. Teaching presence begins with curriculum
and course planning and design. It then continues throughout the delivery of the content of thecourse/program to promote active learning through the use of appropriate teaching tactics andassessments. Design and organization, nurturing conversation, and uninterrupted instruction
influence the learner experience. Organization and design are related to course preparation andscheme, assimilating assessment, exercises, quizzes, and assignments, along with theadministrative aspects of education. Facilitating discourse, the second category, tries to retain
learner interest, motivation, and participation in an active learning environment. It takes control
of connecting material to occasions wherein learners interact and socialize. Direct instruction
is another aspect of TP. According to Anderson et al. (2001), direct instruction occurs when
the teacher provides scholarly and academic leadership. Based on the above literature, thestudy’s second hypothesis (H2) postulated that there is a statistically significant positive
relationship between TP and LE among language learners.

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Social PresenceSocial presence (SP) is an important element in e-learning. It refers to the learners’ ability topresent their ideas publicly and to interact in their learning environment by building interpersonal relationships and expressing their personalities (Garrison & Arbaugh, 2007). SP isconnected with the use of technology for learning purposes and it controls how social
interactions unfold in online environments (Song & Yuan, 2015). It also affects learning
outcomes. It is a crucial affective component as well as an important construct influencing theintensity of communication and efficacy of learning in e-learning (Mykota, 2017). It is
identified as a system of personal relationships engrained in groups by roles andresponsibilities, principles and expectations, and mind-sets and requirements (Annand, 2011).
In workgroups, it is a system of personal relations and is linked to sociability and space. The
study’s third hypothesis (H3) stated that there is a statistically significant positive relationshipbetween SP and LE among language learners.
Learning Presence
Learning presence (LP) is a cyclical process, wherein the learner plans for a language learning
activity, monitors his performance, and reflects upon the consequences. The cycle recurs as thelearner adjusts and prepares for the next activity. Recent studies propose the inclusion of LP as
the fourth element in the CoI framework as a way to denote learner self-regulation (Wertz,
2022). Researchers maintain extant studies on self-regulation offer a solid ground for the
inclusion of LP for enhanced appreciation of the LE (Huang et al., 2019). Hunag et al. (2019)
proposed that factors such as behavioral, cognitive, and motivational concepts and self-efficacy
encourage e-learner experience. In this context, self-efficacy is regarded as a subjective
estimate of the ability to learn. It accentuates the boundaries between cognition and motivation,
being a personal judgment of learners’ competence level in performing tasks/acts (Shea &Bidjerano, 2010). Relatedly, self-regulation refers to perseverance and the aptitude forconfronting setbacks in the completion of tasks about learning (Shea & Bidjerano, 2010). LP
was utilized to evaluate learners’ opinions of their efficacy and effort, giving e-learning a more
"self-directed" impression; Self-efficacy and self-regulation were added as additional scales
for this purpose (Wertz, 2022). Self-regulation denotes the extent to which learners feel they
are motivationally, metacognitively, and psychologically dynamic contributors to the learning
process (Zimmerman, 2008). To effectively complete language-related team projects, learners
self-regulate their activities, divide duties, manage time, and set goals. As such, LP plays asignificant part in enhancing the LE of learners using TELL. Thus, this study’s fourth
hypothesis (H4) stated that there is a statistically significant positive relationship between LP
and LE among language learners.
Emotional Presence
Following an extensive review of the literature on the experiences of learners, emotionalpresence (EP) was added to the CoI framework's conceptual aspects. According to ClevelandInnes and Campbell (2012), emotion is experienced by learners as a distinct presence. This
presence refers to much more than just an affective response to social presence as it enhances
the overall e-learning experience. EP denotes the external manifestation of feeling, affect, andsentiment in a CoI framework as learners communicate and interrelate with e-learning tools,
content, peers, and teacher (Cleveland-Innes & Campbell, 2012). Emotion remains central tolearning as it offers attention, interest, motivation, and social connection. When emotions are
not properly regulated, learners may fail to express, assess, or modulate their approach, thus

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impeding other cognitive experiences causing emotional hijacking (Cavanagh, 2016). Basedon this information, the study’s fifth hypothesis (H5) stated that there is a statisticallysignificant positive relationship between EP and LE among the language learners.
Technological Presence
The present research adds one more presence to the CoI framework: technological presence(TechP). Technology is an essential influence in scientific and social progress. This concept
implies that technology has both potentiality and actuality. In terms of potentiality, technology
provides potential actions through which learners may realize specific actions and, more
importantly, themselves. It is through TechP that learners may become familiar with theirprospects to be in, and act in, the technology enhanced environment. As such, instead of
focusing only on the actual use of technology, its influence must be explored through the notionof TechP. TechP “offers us opportunities, possibilities, and reveals to us potential actions,potential forms of life, and potential ways of relating to our social and physical surroundings”(Kiran, 2012, p. 93). TechP requires competence/self-efficacy, which is imperative for aneffective learning environment based on technology. It is similar to Bigné et al.’s (2019) Digital
Competence Framework.
In the present study, TechP denotes language learners’ perceptions of their ability to use
technology-related sites and tools to perform e-learning activities and tasks to attain desiredlearning outcomes. Researchers have found that technical knowledge has a strong beneficial
impact on adoption and use of technology (Celik & Yesilyurt, 2013). TechP may be used as anindicator of an individual's intent to use technology (Kiran, 2012). Language learners need tobe digitally competent for the completion of learning tasks and activities; as such, they alsoneed to possess information and data literacy, digital content comprehension, and problemsolving. Learners with higher technological knowledge are more likely to be persistent, as they
do not give up easily and try to attain their language learning objectives both inside theclassroom and beyond (Lai, 2013). Language learners’ TechP will be echoed in theirtechnological skills while performing technology-based tasks (Mew & Honey, 2010), and it
may significantly influence their intent to utilize e-learning facilities and applications. Thus,
TechP may indicate learners’ opinions of technology as beneficial and simple to use, andconsequently their desire to utilize it for self-directed learning (Lai, 2013). Based on thisliterature, the study’s sixth hypothesis (H6) stated that there is a statistically significant positive
relationship between TechP and LE among language learners.
Technical Barriers
Researchers regard technical issues to be critical for e-learning (Kauffman, 2015). Technicalissues and problems have been identified as technical barriers (TB). Internal barriers include
things like attitudes and beliefs, while external barriers include time, technical and institutional
support, and infrastructure. The lack of these elements as well as want of suitablecommunication strategies for their implementation has been identified as a reason for poor
technology implementations (Alvi, 2022). Therefore, TB may refer to any existing belief that
restrains usage intentions and promotes rejection of technology (Cenfetelli & Schwarz, 2011).
Learners' perceived technological intricacy, which refers to the quality/state of being complex
or being problematic/difficult to use, can also be highlighted as a barrier (Ali et al., 2018).
Thus, while technology might help students learn more effectively, it can be a hindrance insome cases (Lane, 2019). Therefore, the study’s seventh hypothesis (H7) stated that there is astatistically significant positive relationship between TB and LE among language learners.

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Research Methodology
Instrument Design
To collect information from language learners, an e-questionnaire was drafted. This online
questionnaire was separated into two sections. The first section collected student demographic
information such as gender, age, and the standard /class in which the learners were enrolled.
The second section of the e-questionnaire consisted of 59 items for assessing and quantifying
forms of presence. The CoI measurement scale (TP, CP, and SP) was based on studies by
Arbaugh (2008) and Swan et al. (2008). The existing scale was extended by adding five new
constructs based on existing literature, namely LP (Wei et al. 2020), TechP (Tetri & Juujärvi,2022), TB (Akhter et al., 2022), EP (Cavanagh, 2016), and LE (Woodcock et al., 2015). All of
the items were adapted, adopted and modified for the current study. The items were based ona five-point Likert-scale where 1 indicated “strongly disagree” and 5 “strongly agree”.
Participants and Research Design
The study’s population included learners enrolled in language learning courses during the2021–2022 academic year. The potential participants were all undergraduate students, studyingin the first semester at an institute in Rajasthan, India. They had been using technology for
language acquisition for at least six months. Data was collected from learners after seeking
approval for the study from the ethics committee for the study of human subjects. As the study's
research methodology is cross-sectional, a quantitative technique using an e-questionnaire was
employed to obtain responses from language learners.
Statistical software (G*power 3.1.9) was used to calculate an adequate sample size (Faul et al.,2007) for correlation analysis. The following settings were utilized for a two-tailed test for
medium effect size: α err prob = 0.05, power (1-β err prob) = 0.80 and correlation ρ H1 value= 0.3. As per the results, the minimal sample size for the investigation was 84 with an error
probability of 0.05 and a confidence level of 80 percent. Based on these findings, the sample
size of 129 was considered sufficient for the present study. The sample was chosen using arandom sampling procedure, which ensured all learners in the population had an equal chanceof being selected. The sample comprised of approximately 70% males and 30% females. The
average age was 18 (SD = 1.87). The participants came from diverse backgrounds with nearly
60% from urban parts of Rajasthan and 40% from rural regions.
Analysis of Data
Data analysis used descriptive statistics to calculate frequency, distribution, percentage, mean,and standard deviation. Statistical analyses were carried out for verifying scale and instrument
reliability. Validity of the instrument was checked by considering both the rationality andaccuracy of each question’s wording (how well and accurately it conveys the intended
message) and the validity of the responses it elicits (how well it captures respondents’ trueideas). The study used well-proven instruments. Moreover, a pilot study was conducted using
20 respondents to test the research instrument. The accuracy of the items was checked and a
few minor changes were made to ensure the accuracy and validity of the instrument based on
feedback received from the respondents. Finally, inferential statistics were used for hypotheses
testing.

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Results
The first step in the process was to use Cronbach's alpha (CA) to measure the instrument
reliability. The total reliability of the instrument was above 0.90, which indicated the scale
used had excellent overall reliability. The values of CA for TP, CP, SP, EP, LP, TechP, TB,
and LE are presented in Table 1. The results affirm the constructs were reliable as all CA
values’ were above 0.83. Next, the study sought to scrutinize the level of TP, CP, SP, LP, EP,
TechP, and TB among language learners in India. The mean score, called the average score,
was obtained by totaling the sum of the data sets divided by the number of items for eachconstruct. As shown in Table 1, the mean scores ranged from the lowest for TB (2.97) to thehighest for TP (4.47). The standard deviation, Std.D, which refers to the average amount ofvariability in the dataset, was also calculated. Next, skewness, which denotes the degree ofimbalance in the frequency distribution, and kurtosis, which denotes the degree of tailed-nessin the frequency distribution, were observed. The values for kurtosis and asymmetry ranged
between -2/+2 which are taken as satisfactory (George & Mallery, 2010). As seen, these values
ranged from fairly symmetrical for TB (between -0.5 and 0.5) to moderately skewed (0.5 and1/-0.5 and -1) for most of the constructs, to highly skewed (greater than -1) for LP. Means,standard error, standard deviations, and Cas for the constructs are summarized in Table 1.
Table 1Means, Standard Error, Standard Deviations, and Cronbach Alphas for the Constructs

Teaching PresenceSocial Presence
Cognitive Presence
Learning Presence
Emotional Presence
Technological Presence
Technical Barriers
Learning Experience

CA
0.93
0.89
0.89
0.84
0.83
0.93
0.84
0.88

Mean
4.47
4.10
4.31
4.44
4.33
4.30
2.97
4.38

Std. Error
0.05
0.06
0.05
0.06
0.05
0.05
0.12
0.05

SD
0.53
0.68
0.58
0.64
0.61
0.59
1.38
0.57

Skewness
-0.84
-0.50
-0.59
-1.02
-0.67
-0.61
-0.01
-0.76

Kurtosis
0.01
-0.47
-0.48
0.13
0.20
-0.39
-1.39
0.27

Table 2 reports the item-level means, errors, standard deviations, skewness, and kurtosis. The
mean for items ranged from 2.81 for TB6 to 4.53 for TP10.
Table 2Item-Level Means, Errors, Standard Deviations, Skewness, and Kurtosis
Construct

Teaching Presence
(TP)

Item
TP1
TP2
TP3
TP4
TP5
TP6
TP7
TP8
TP9
TP10

Mean
4.52
4.50
4.49
4.47
4.42
4.44
4.49
4.40
4.39
4.53

Std. Error
0.06
0.06
0.06
0.06
0.06
0.07
0.06
0.06
0.07
0.06

79

SD
0.63
0.64
0.64
0.65
0.68
0.74
0.70
0.69
0.78
0.69

Skewness
-1.14
-0.90
-0.87
-0.82
-0.91
-1.04
-1.15
-0.85
-1.10
-1.73

Kurtosis
1.17
-0.24
-0.28
-0.38
0.27
0.05
0.56
0.13
0.49
4.54

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Social Presence (SP)Cognitive Presence(CP)

Learning Presence(LP)
Emotional Presence(EP)

Technological
Presence(TechP)Technical
Barriers(TB)

Learning Experience
(LE)

TP11
SP1
SP2
SP3
SP4
SP5
SP6
SP7
SP8
SP9
CP1
CP2
CP3
CP4
CP5
CP6
CP7
CP8
CP9
CP10
CP11
CP12
LP1
LP2
LP3
EP1
EP2
EP3
TP1
TP2
TP3
TP4
TP5
TP6
TP7
TP8
TP9
TP10
TB1
TB2
TB3
TB4
TB5
TB6
LE1
LE2
LE3
LE4

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4.50
4.23
4.31
4.26
3.93
3.97
4.06
3.95
4.12
4.07
4.26
4.36
4.40
4.26
4.37
4.31
4.37
4.23
4.33
4.24
4.28
4.32
4.46
4.46
4.42
4.41
4.30
4.28
4.40
4.33
4.32
4.34
4.40
4.19
4.16
4.35
4.30
4.21
3.09
2.95
2.88
3.09
3.03
2.81
4.41
4.44
4.40
4.42

0.06
0.07
0.06
0.08
0.10
0.10
0.09
0.08
0.07
0.08
0.07
0.07
0.06
0.07
0.07
0.07
0.06
0.07
0.07
0.06
0.07
0.06
0.07
0.07
0.06
0.06
0.06
0.06
0.07
0.06
0.06
0.06
0.06
0.07
0.07
0.06
0.07
0.07
0.13
0.13
0.13
0.13
0.13
0.14
0.05
0.06
0.06
0.06

80

0.68
0.77
0.73
0.87
1.16
1.10
1.05
0.88
0.79
0.91
0.79
0.82
0.72
0.82
0.75
0.76
0.73
0.78
0.76
0.73
0.79
0.68
0.74
0.76
0.73
0.65
0.73
0.73
0.73
0.70
0.68
0.66
0.67
0.81
0.84
0.68
0.81
0.83
1.47
1.50
1.42
1.48
1.48
1.56
0.61
0.67
0.70
0.68

-1.18
-0.52
-0.55
-1.18
-1.05
-1.07
-0.95
-0.53
-0.89
-0.96
-0.70
-1.53
-0.89
-0.87
-0.96
-0.92
-0.95
-0.84
-1.27
-0.40
-0.83
-0.65
-1.09
-1.53
-0.96
-0.64
-0.66
-0.86
-1.28
-0.55
-0.50
-0.49
-0.68
-0.55
-0.56
-0.57
-1.15
-0.75
-0.12
-0.03
-0.04
-0.12
-0.05
0.24
-0.50
-0.96
-0.87
-0.75

0.79
-0.81
-0.93
1.22
0.41
0.58
0.02
-0.08
1.37
0.88
-0.42
3.17
-0.07
0.06
0.22
0.41
0.41
0.38
2.46
-1.02
-0.02
-0.06
0.13
2.85
0.01
-0.57
-0.36
0.65
2.40
-0.82
-0.79
-0.69
-0.60
-0.75
-0.71
-0.74
1.51
-0.22
-1.41
-1.46
-1.34
-1.41
-1.42
-1.50
-0.62
0.43
0.06
-0.57

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LE5

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4.24

0.07

0.79

-0.75

-0.12

Next, the Shapiro-Wilk multivariate normality test was conducted to assess the data's normality
assumption (Shapiro-Wilk value = 0.89). There was no normal distribution for any of thesignificant outcomes (p < 0.001), indicating that the normality assumption was violated.
Finally, the Spearman's Rank Correlation test, which is a gauge of how well variables are
related, was conducted. The strength of the link between the respondents’ perceptions and theirexperience was measured utilizing Spearman's Rho (ρ). Positive correlations are signified by apositive value in Spearman's Rho analysis, with stronger correlations being closer to one.
Negative correlations, on the other hand, are signified by a negative number, with stronger
correlations being closer to a negative one. If the result is zero, there is no correlation between
the variables. Statistical significance was also examined within each variable pairing. The ‘α’was set to 0.05, meaning the confidence level was 0.95.
Table 3
Hypotheses Testing Using Spearman's Rho
Hypotheses
H1: Teaching Presence àLE
H2: Social Presence àLE
H3: Cognitive Presence àLE
H4: Learning Presence àLE
H5: Emotional Presence àLE
H6: Technological Presence àLE
H7: Technical Barriers àLE

Spearman's rho ρ
0.67**
0.65**
0.75**
0.65**
0.70**
0.73**-0.01

p< .001< .001< .001< .001< .001< .001
0.89** p < .001

The results of the study’s hypotheses are presented in Table 3. Of the study’s seven hypotheses
framed, postulating that there should be a statistically significant positive relationship between
various presence and LE, six were validated. H1: TP and LE (ρ=0.67, p <0.001); H2: SP andLE (ρ=0.65, p <0.001); H3: CP and LE (ρ=0.75, p <0.001); H4: LP and LE (ρ=0.65, p <0.001);
H5: EP and LE (ρ=0.70, p <0.001); H6: TechP and LE (ρ = 0.73, p <0.001). These hypotheses
were accepted based on the outcome of the Spearman's correlation analysis. However, thefindings revealed no significant relationship between TB and LE. Although the relationshipbetween TB and LE was negative (as anticipated), it was not statistically significant (ρ = -0.01,
p = 0.89). Based on these results, H7 was rejected. These results are summarized in Figure 2.

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Figure 2
Research Model Showing Construct Means, Standard Deviations, and Associations with LE

Discussion
The current study sought to scrutinize the factors that impact LE in the context of TELL in
India. More specifically, it examined the level of TP, CP, SP, LP, EP, TechP, and TB among
Indian undergraduate language learners. Comprehending the level of these variables isimportant as they influence the LE and ensure language learning communities offer a secure,encouraging atmosphere without inhibitions for learners.
Based on descriptive statistics, the average mean obtained by totaling all items of eachconstruct, confirmed that the level ranged from lowest for TB to highest for LP. Moreover, thelevels were relatively high for all forms of presence. The findings revealed that language
learners perceive LP as the most significant presence, followed by other forms, while TB wasperceived as the least significant among the selected variables. The study also affirmed theassociations between the three original factors in the CoI framework, thereby augmenting priorstudies (Dempsey & Zhang, 2019).
Next, the study confirmed the comprehensive and inclusive proposed framework by extending
the CoI framework. Additionally, it experimentally and analytically validated the proposed
framework in the context of language acquisition. It was discovered that TP may contribute
positively to LE, implying that TP is vital for improving language learners' learning
experiences. TP denotes the significance of course content, activities, and mentorship(Caskurlu, 2018). The findings suggest that each aspect of TP, which includes course content,
activities, and mentorship, is critical for ensuring L2 learning is made easy for learners. The
results also suggest that TP contributes to the LE because language teachers play a key role intriggering learning through appropriate course conception, topic covering, and efficient
feedback and communication mechanisms (Caskurlu, 2018).
SP was found to be considerably related to LE. As such, it may aid in the development ofinteraction among the learners which further enhances their interpersonal relationships (Zhou,2016). This happens by increasing learners’ engrossment and contribution in the learning

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environment and collaborating with their peers for a mutual objective (Hilliard & Stewart,2019). These findings imply language learners can express discrepancies, exchange opinions,study contrasts, and acknowledge support and encouragement from peers and teachers throughtheir SP. In other words, language skills relevant to their cultures have to be acquired to enable
them to debate and defend their viewpoints. These abilities can be honed through supportive
dialogue and the creation of a positive learning environment by the teachers. For this objective,
course design and facilitation are important since they ensure learners feel more engaged inlearning and improve their critical thinking (Chang et al., 2015). In short, SP delivers
prominence to communication and collaboration (Shea & Bidjerano, 2009).
In the context of Indian students, the study discovered that among the select constructs, CP had
the strongest association with the language LE. This implies that students prefer to learn
through cognition. Language teachers may ensure CP by utilizing interactive activities basedon real or virtual scenarios in the language course. CP specifies an inquiry-learning process,comprising identifying the problem, coalescing ideas, and scrutinizing conceivable solutions.
These findings contradict the results of some prior researchers who found a weak correlation
between CP and learning (Martin et al., 2022). Since CP aids in the discovery andunderstanding of learners’ identity depending on their needs, it is important for language
learners. To improve performance, learners use their reflection on subjective experiences(subjectification) for producing action (pragmatic). This shows that students’ inherent
conscious intelligence to learn may outweigh the effect of external factors on their intellect.
Thus, TP, SP, and CP in the CoI framework emphasize the e-learning progression andconcentrate on LE. Group cohesion, collaboration, and communication play a significant role,as parts of SP (Garrison & Arbaugh, 2007; Kreijns, 2022). In this view, the learner needs tostay cognitively involved and endeavor to learn efficiently in the social space in which theteacher plays a substantial role. The cumulative influence of all of these elements provides thelearners with an influential LE (Arbaugh et al., 2008). As such, the interrelationship between
the original three variables was investigated and the results affirmed prior research, which was
based on undergraduate and post-graduate data (Heilporn & Lakhal, 2020; Garrison, 2010;
Dempsey & Zhang, 2019).
Further, the findings indicate strong, positive, and significant associations between the newly
added constructs (LP, EP, and TechP), with the exception of TB. This suggests that students inonline language learning courses are knowledgeable in e-learning technologies, have requisite
technical abilities, and feel relaxed in e-learning environments. LP emerged as a key predictorof LE, demonstrating that it must be addressed by the teachers in the context of language
learning. Further, the learning experience needs to be made interesting and entertaining toensure learner involvement and engagement, which confirms previous studies (Wertz, 2022).
The association between LP and LE was positive and significant; the findings affirm Ma etal.’s (2017) study. The findings also confirm Lin et al.’s (2015) study indicating that LP, which
is tantamount with self-efficacy, plays an important part in CoI.
EP was found to be empirically linked with learning. It has also been found to be linked withcognition (Thomas et al., 2017). As a learning environment induces constructive or disparaging
sentiments in learners, EP may influence the quality of LE. Negative feelings can confuse
learners (Cleveland-Innes & Campbell, 2012), and adversely influence their LE. Therefore,teachers need to be wary of the presence of any negative feelings among language learners. In
order to enhance EP, teachers may offer motivation, care, and understanding to learners (Green& Batool, 2017). Ideally, this will make them feel safe and appreciated, ensuring they also feel
connected, which may boost their confidence.

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TechP represents the learners’ attitude towards the use of technology and is the learners’
affective response to e-learning while demonstrating their experience related to its use. In thisstudy, TechP represented learners’ affective response to using technology for language
acquisition. It was found to be positively associated with LE. However, it must be noted that
TechP may differ according to field of study. Nevertheless, learners’ abilities to speak andunderstand English may offer them more understanding and efficiency in e-learning
environments.
To summarize, the present study empirically tested and validated the CoI framework
concerning language learners in India. It confirmed that presence contributes to LE (Caskurlu,
2018). It emphasized the multi-dimensionality of the CoI elements, and establishedfundamental relations between them using a correlational model. This study extended the CoIframework by delivering an improved framework for studying and investigating language
learners’ experiences. This was done by including four more constructs (learning presence,emotional presence, technological presence, and technical barriers). The extended andmodified framework, which depicts the many facets of the learning process that make up theLE, suggests that meaningful and substantial learning occurs when learners’ expectations
concerning all forms of presence are satisfied. Finally, the results of the study may ensure
TELL courses are designed as vigorous learning environments and communities in which
students, and teachers, exchange knowledge and views, besides ideas and experiences(Picciano, 2017).
Theoretical and Practical Recommendations
The findings of this study have a number of implications. Firstly, the study provides teachers,
administrators, and policymakers guidance on how to recognize the elements that affectlanguage learners’ perceptions and experiences. This information can be used to enhancelearner satisfaction and experience by applying the CoI framework to TELL. The proposed
extended CoI model explains how learners experience presence and the associations between
various constructs. This research has theoretical implications as it adds to the body ofknowledge about e-learning courses and learner experience. To the best of the author's
knowledge, it is the only investigation to incorporate four contextual elements (LP, EP, TechP,
and TB) into the CoI framework. The research reveals how these factors influence LE for
language learning purposes, thereby playing a critical role. It shows that CP, SP, and TP havean impact on LE, proving the validity of the CoI framework. The comprehensive CoI model isfurther extended and validated using raw data from language learners; thus, it contributes
significantly to the extant literature on LE. This work has significant theoretical implications
because the findings elucidate learners’ perceptions of utilizing CoI. The modifiedcomprehensive and inclusive model results in a more powerful illustrative model, thereby
adding to the extant studies encompassing CoI by experimentally investigating LE. It fills theresearch gap and addresses a significant area of investigation about e-learning and language
learning. However, the findings contrast with previous research, indicating that TB isn’t anobstacle in the perception of language learners in India (Pillai, 2020). Further research isneeded to address this discrepancy using larger and more varied samples, as well as moreadvanced statistical and inferential methodologies. As such, the results should not be overly
generalized.
Finally, the present research has practical implications as it reveals the significance of CP, SP,
TP, LP, EP, and TechP, for ensuring students' LE through e-learning, course gamification,interaction, and blended learning. All of these course formats combine virtual and realengagement for enhanced LE. Both SP and TP are vital, so instructors may ensure language

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content is engaging enough for the learners and provide them with ample opportunities toengage in team activities, for promoting a more satisfying experience. Another major point
raised in this study is the importance of LP and TechP. Learners must feel satisfied and content.
They must feel joy, excitement, and playfulness and be technically competent. Instructors may
guarantee learner satisfaction by introducing more communicative activities. In the context ofa developing nation like India, instructors may pay more attention to the course design and its
delivery to cater to the needs and demands of a diverse population of language learners. These
results may also aid teachers, administrators, policymakers, and governments, as well as privateorganizations, in providing better facilities and resources for language learners in India. As
such, academics, policymakers and researchers may find the CoI framework a useful tool forfurther study of L2 learning using technology, in various contexts.
Conclusion
The present investigation makes numerous contributions to the CoI literature. The study
assessed the associations between CP, SP, LP, EP, TP, TechP, and TB. A comprehensive CoImodel incorporating seven variables in the context of language learning was proposed andvalidated. In addition, it used a different methodology by using Spearman’s rank correlation
coefficient to validate seven hypotheses about the association between these constructs and LE.
The proposed model may be useful for identifying the factors affecting learners' experiences
and may provide valuable implications and recommendations for enhancing LE for language
learners. However, the findings may be further tested using more advanced statistical andanalytical techniques, including qualitative methods such as interviews or open-ended
questions. Such methods would provide more in-depth coverage of learners’ experiences, sincelanguage learning experiences may be more suitable for qualitative methods. The instrument
also needs to be validated in other contexts using larger sample sizes and more advanced
analytical methodologies. Moreover, the generalizability of the results can’t be done without
skepticism, as the results may differ based on context or culture. Another limitation of the study
is that it is based on self-reported data which depends on the respondents’ willingness to
respond accurately. Learners may not be eager to respond accurately, which might bias theresults. Lastly, the information was collected from learners only. Future studies may focus onother stakeholders like policymakers, government officials, and teachers. To conclude, thefindings of the study have significant reference value for expanding the inquiry community's
framework, comprehending the learners’ experience, analyzing the influence of presence, andenriching the application of the CoI framework, thereby confirming that presence can reduceor enhance the learning experience. The results also may ensure TELL courses are designed as
vigorous learning environs which facilitate language acquisition.

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Corresponding Author: Irum Alvi
Email: irumalvi@gmail.com

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Immersive Technologies in Physical Education in Malaysia for Students
with Learning Disabilities
Nur Azlina Mohamed Mokmin
Universiti Sains Malaysia, Malaysia
Nurul Nabilah Izzati Binti Ridzuan
Universiti Sains Malaysia, Malaysia

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Abstract
Physical education (PE) is a compulsory subject in schools that is vital to ensure students stayhealthy and fit. It is widely agreed that everyone should have access to education, regardlessof physical limitations. However, there is an ongoing debate on how PE has been taught andused to benefit students with learning disabilities (SLD). SLDs may feel excluded from PE due
to lack of support systems, low motivation, and unsuitable learning materials. This paper
discusses the development of an app that applies immersive technologies to make PE accessible
to SLDs. Virtual reality and augmented reality are immersive technologies in which the user
can view virtual media to understand objects in the real world. With these technologies,
students can freely control their learning progress and choose the content critical to them basedon their circumstances, enabling instruction based on their ability. Optimal Motor Learning
Theory and Cognitive Theory of Multimedia Learning (CTML) were used to develop the app.
The first phase of this two-stage research process involved creating a VR app and getting
feedback from the students who used it. Interviews of students who used the VR trainer andsecondary data were used to inform the development of an AR book. The authors found thatby combining suitable immersive technologies with motor learning theory and multimedia
learning principles, appropriate learning material could be created that facilitates the PE
learning process.
Keywords: augmented reality, immersive, physical education, sport, strength exercise, virtual
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Physical Education (PE) is an elementary and secondary school subject focusing on the humanbody and psychomotor learning (Andrieux & Proteau, 2016). The course develops skills,
knowledge, values, and attitudes required to establish and enjoy an active and healthy lifestylethrough extensive learning. Face-to-face learning builds students’ confidence and ability toface individual, group, or team challenges (WHO, 2020). In the right PE setting, the lessons
can engage students, attract distracted learners’ attention, and create an environment thatcultivates enthusiasm for physical activities.
The COVID-19 outbreak forced many countries to restrict movement and required education
to be home-based and/or online. Schools in 194 countries were closed, and many virtualteaching and learning activities were organized to ensure the learning process continued toachieve the targeted learning outcomes. In European countries and the United States, physicaleducation teaching activities were carried out via live streaming, providing students withrecorded videos or links to follow on their own (Filiz & Konukman, 2020). However, physicaleducation is a minor subject in Malaysia that gets little government attention. Resources areallocated to mainstream subjects (Tan, 2021), such as compulsory mathematics and science,and included in national exams.
Many educators found that teaching PE online during the current pandemic was challenging tohandle (Varea & González-Calvo, 2020). The teaching process became less physical. Thisapproach also made students less healthy and increased sedentary behavior among otherwise
active students (Roe et al., 2021). PE needs observational learning and physical instruction,
which adds to teachers’ difficulties in ensuring the learning process is maintained or mimics
that in a typical classroom before the pandemic. Nevertheless, the students’ involvement inphysical, sports, recreational, and social activities is a must to produce fit and productive
students (Lim et al., 2016). However, there is an ongoing debate on how PE has been taught
and used to benefit students with learning disabilities (SLD). Roe et al. (2021) suggest that PE
teaching and learning can be carried out successfully at a distance with suitable strategies,
including personalization, creativity, and inclusiveness. This paper discusses the developmentof an app that applies immersive technologies to make PE accessible to SLDs.
Background and Literature Review
According to data from the Department of Special Education, 82 percent of students withdisabilities (SWD) in Malaysia in 2020 were classified as having a learning disability (LD).
The significant number of SWD with learning disabilities raises issues about the inclusivity of
SWD in the Malaysian Education system (Nordin et al., 2019). SWDs that have been assessed
as having autism, attention deficit hyperactivity disorder (widely known as ADHD), dyslexia,
writing disorders, and dysphagia are classified as students with learning disabilities (SLD)[Radzi et al., 2019]. The Ministry of Education uses the term SLD to characterise pupils who
cannot learn in a traditional classroom setting. They have poor reading, writing, and arithmetic
skills. Intellectual dysfunction, neurological abnormalities, or neurological processing
difficulties may cause their learning disabilities (Nordin et al., 2019).
These students also face difficulties during the learning process in PE (Hamizi et al., 2022).
According to Di Palma et al. (2018), among the problems are fears of participating, insufficientlearning resources, lack of school-based support, and insecurities with others. These difficulties
can hinder their true potential in PE and make some learning outcomes impossible to achieve.
Therefore, educators should consider the students’ unique demands and emphasize improving
their concentration and creating a flexible learning environment.

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Theories of Motivation and Learning
Wulf and Lewthwaite (2016) introduced the Optimizing Performance Through Intrinsic
Motivation and Attention for Learning (OPTIMAL) theory of motor learning, which argued
that intrinsic motivation and learning, attention, performance, and knowledge could contributeto motor performance and physical education learning. According to the theory, to increase
motivation, it is essential to maintain student autonomy in terms of control over the learning
process that can ultimately lead to focusing on the goal. The theory also posited that giving
learners control over specific practice areas or using assistive technology can improve motor
learning. Combining confidence in their potential to perform effectively with instructions thatcan raise their external focus can help students achieve successful movement results. These
will boost self-efficacy and help solidify the positive impacts of performance, producing avirtuous circle that will have benefits for learning and motivation in the long run.
Learning elements that increase expectations for future completion and support autonomy
through perceived control and motivational mediators like self-efficacy, perceived
competence, and positive affect accelerate motor learning. Dopamine is released when thesemotivational variables are met, which aids in brain circuit growth and memory consolidation(Puig et al., 2014). Furthermore, combining OPTIMAL variables increases motor learning byenhancing goal-action coupling efficiency (Wulf & Lewthwaite, 2016). Figure 1 displays the
Optimal Theory of Motor Learning. Autonomy and enhanced expectancies are illustrated as
motivational, while the external focus is an attention factor that improves learning andperformance of motor skills. Autonomy support describes a situation in which a person is given
the ability to control or choose certain aspects of practice or performance conditions. Focusing
on the movement goal or effect, such as focusing on the dartboard while throwing a dart, is an
example of an external focus of attention.
Figure 1
The Optimal Motor Learning Theory (Wulf & Lewthwaite, 2016)

PE has significant effects on the overall growth of SLDs. However, researchers have foundseveral issues the SLDs face in PE (Di Palma et al., 2018; Greguol et al., 2018; Tafuri &Cassese, 2017). According to Kohli et al. (2018), some SLDs have been given inadequate
learning materials, are afraid to mingle due to a lack of social skills, are nervous when
performing PE, and lack institutional assistance. SLDs’ participation in PE classes is also lower
than other same-age students in primary and secondary education (Adams, 2016). Thus,

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educators must consider the needs of SLDs while providing them with superior PE learning
experiences. Educators can work on the necessary modifications to the curriculum and classsettings that benefit the students. In a physical education class, better learning guidance makes
PE enjoyable and provides positive results. However, there is still a lack of assessment of PE
teaching and learning effectiveness in Malaysia and whether the implementation has beencatering to the SLD.
Mayer’s (2011) Cognitive Theory of Multimedia Learning (CTML) proposes that an elemental
overload will occur when cognitive processing exceeds the learner’s ability, thereby inhibiting
learning. Previous research has shown that when a multimedia course is presented in immersive
VR, learners are more likely to experience complete overload than the same course in nonimmersive media because the external load increases due to the rise in sensory information(Freeman et al., 2019). This model will be used to distinguish two kinds of cognitive load forthe design process of immersive multimedia learning in physical exercise. The first is theinherent cognitive load related to the properties of the learning material, such as its intrinsic
difficulties. The other is an unrelated cognitive load associated with the presentation of thematerial. The CTML is used as a guide alongside the OPTIMAL Motor Learning theory indeveloping the multimedia design for the app.
Immersive Technologies in Learning
Immersive learning uses enhanced simulated or purely artificial environments so that learners
can experience scenarios and simulations to produce valuable and engaging knowledge. This
technology is becoming popular as it offers learning outcomes by providing authentic
experiences. In the field of instructional technology, the terms “virtual reality” and “augmented
reality” have been trending for the past five years (Kimmons, 2020). For physical education,
the potential usage of VR is limitless. In their study, Jiao and Qian (2020) used VR to teach PE
by displaying various videos using the Flipped Learning approach. Their research shows thatcombining VR and Flipped Learning significantly increased PE students’ satisfaction
compared to traditional classroom learning. Another study on the application of VR for PE
conducted by Brooke et al. (2020) proved that the technology could be used to teach badminton.
Their research concluded that VR-based PE training could: (a) improve understanding, (b)teach repeated practice, (c) increase teamwork and mutual support, and (d) promote motivation.
In addition, Mokmin (2020) and Mulders et al. (2020) concluded that VR technology could
provide additional stimulation to encourage students to engage in PE whenever teacher
supervision is unavailable.
Virtual reality often uses a headset to shut out visual stimuli from the outside world. In thisimmersive alternative, the user can pick up and move objects, turn on and disassemble gadgets,stroll around a room, and interact with virtual characters. Not only can virtual reality
technology be utilised to teach knowledge, but it can also be used to teach actions and skills.
As long as there is a suitable model library, students can put on virtual reality glasses and watch
the steps of action in person. Because this process is computer-controlled, students can watch
it repeatedly, slow it down, study it from different angles, or even engage in the action. Students
can freely control the pace of instruction and choose the content that is critical to them basedon their circumstances and ability (Wu et al., 2021). Virtual reality (VR) allows physicallydisabled people to try out-of-reach experiences like climbing a mountain, extreme sports, orswimming in the sea for the first time.

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Mokmin and Jamiat (2020) discussed the development of a virtual fitness trainer to motivate
undergraduate students to engage in physical exercise. Their study showed that most
respondents gave positive reviews of the fitness activities with the trainers, and their motor
performance increased when learning with the app. However, their design has not been tested
for SLDs. Reducing the cognitive load during learning, especially for SLDs, is vital to engage
them in learning (Hardiyanti & Azizah, 2019). Therefore, the CTML is used to guide thedevelopment process so that the multimedia presented to the students can improve theirlearning achievement.
People with special needs may benefit from augmented reality (AR), which can help them build
everyday life skills. AR allows users to see the real world while overlaying virtual elements on
top of it. According to a meta-analysis by Baragash et al. (2020), AR is an appropriate learning
medium to enhance the involvement of SWDs in society, teach varied abilities, educate,
diversify physical learning, conduct self-care duties, and retain information for an extended
period. Because AR has the function of displaying context-sensitive digital information, which
can support individual needs at the time and provide timely learning, it is a potent tool for
people with disabilities (Walker et al., 2017). The term “augmented reality” refers to virtual
reality in which virtual things are placed in real-world settings. The objects can be visualisedin front of the users statically or dynamically. This technology allows developers to add labels,
videos, or related information to the displayed objects. It is like having a different environment
on your mobile device. In addition, AR could lead the blind with guided audio.
These two immersive learning technologies can be used separately or in combination to
produce compelling learning experiences. These technologies should be explored as analternative to typical classroom settings, particularly in the event of a disruption in schoolingor immersive learning. The exciting element of immersive learning is that it creates a highly
engaged environment for users, both virtually and physically (Kumar, 2020). This allows an
instructor to effectively reproduce a variety of actual locations that are not accessible within
the confines of a classroom. The virtual content may be so motivating that it has a lasting effecton the learner’s mind (Hennick, 2020). The technology typically uses a headset to block
external visual stimulation (VR) or overlay visuals over the real world (AR). Because thetechnology is available on cell phones, it is being more widely used and thus readily available
to students (McCarthy & Uppot, 2019).
Our research focused on designing and developing an app that used virtual and augmented
reality technology to help physical education students learn more effectively. This study
developed five virtual trainers and one augmented trainer using optimal motor learning theory
and CTML. The following are the study’s primary objectives: (a) to see how effective virtual
physical education coaches are at engaging and motivating pupils to participate in fitness
activities and (b) to design and develop an AR book and AR app for PE based on theinformation collected from objective primary and secondary data. The CTML has been used tocreate the virtual trainer, exercise movements, choose suitable texts, illustrate images, andchoose suitable sounds for the app.
The Design and Development ProcessThis study is a multiphase design and development study with a qualitative evaluation
component. Interviews were used to understand how immersive physical education trainers canfacilitate learning and function as good learning material. The paper aims to answer thefollowing questions: (a) how can a virtual physical education trainer help engage and motivate

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students to learn physical exercise and (b) what could be the design of a suitable AR trainer forphysical exercise for SLDs? This study was divided into two main phases. Table 1 summarizes
the research and design procedure that followed the ADDIE Model.
Table 1
The Design and Development Process
Process
Analysis
DesignDevelopment
Implementation
Evaluation

Activities
Needs analysis from teacher interviews and secondary resources
collection
The design of the five virtual trainer characters and storyboard
The development of the trainers and the app using Unity and
Adobe Mixamo
The implementation of the app with a head-mounted device andtesting in the actual setting
The evaluation of the app using student interviews

Phase 1: The Development of The VR Trainers
A preliminary investigation was done in the school to get an overview and additional
information about the teaching and learning process. Unstructured interviews were carried out
with five teachers. The questions were: (a) what are the most important things that should beconsidered in class, (b) will the students understand the 3D object and learn the movements,(c) what is the maximum number of students for each lesson, and (d) what type of learningmaterials are used in teaching and learning? The teachers listed important things to clarify
related to teaching SLD. The students must be in a group of less than 10 per session and the
instructions must be done step by step. The students also like a trainer that shows actions thatare easy to follow. The needs analysis results were used to select the participants and design
the virtual trainers.
Design and Development
In the first phase, five virtual trainers were developed to test how students reacted to the trainingof physical movements using virtual trainers. Five virtual fitness trainers were created to see
how effective virtual physical education coaches engage and motivate pupils to participate infitness activities. These trainers were developed based on the suggestions from the teachers.
The teachers suggested that the trainers show the training steps from the preliminary
investigation. The activities must also be fun and attract the students’ attention. Thus, the five
trainers’ movements were unique. They were created based on the optimal motor learningtheory, which proposes that students learn motor movements by observing the trainers and thesigns of motor actions shown explicitly by the trainers. Different trainer types with animations
were used to increase student motivation and interest in physical activity. According to motor
learning theory, specific motor techniques can be gained from training the body to perform
precise motor movements. The CTML is applied to the design process of the VR trainer. Table
2 gives the usage description of the multimedia principles used for the design.

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Table 2Multimedia Learning Principles, Definition, and the Application
Multimedia Principle Definition
Usage Description
Coherence
Concise, short, and support the In this design, only the
instructional goalvirtual trainers show the
movements, accompanied bysuitable sound.
Redundancy

Avoid displaying the same content of The trainers only show the
information using audio and text
movement with music, andthe narration is not applied

Contiguity

The text must be close to the visuals The text was placed near thethat are the subjects of training
trainer.

Segmenting

The learning content must be broken The steps were presented indown into lessons
segments and phase by phaseto make it easier for the
students to follow.

Signaling

Learners must be shown exactly Showing exactly what items
what to see on the screen.
they need to pay attention toduring the movements

The first trainer was Sharky in Figure 2, where the character displayed funky and enjoyable
dance movements. Sharky is dressed as a human with a shark costume to attract the learners’
attention. This motion is intended to encourage the learners to warm up before engaging in
more vigorous exercise activities. This was from a report by Anderson and Rastegari (2016)that for students with a disability, dance, especially a happy dance, can encourage them toexercise. AJ’s second character, displayed in Figure 3, demonstrates more intense activities
like running and jumping around. The third trainer is Malcolm in Figure 4, who shows fitness
through fighting techniques like punching and kicking. The fourth is Granny’s character inFigure 5, which focuses more on dancing and easy movement. Although this character is named
Granny, she is a sporty granny with Jazz and Zumba dancing. Figure 6 shows Scarlet’scharacter, which demonstrated vigorous activities in video games like running, zombie
fighting, and high kicking. The average duration of activities with each of the characters isapproximately five minutes. These characters were developed based on the report by Mokmin
and Jamiat, 2020. According to these reports, students with disabilities can learn physicalactivities from adaptive physical exercise suitable to their learning needs.

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Figure 2
Sharky with Happy Dance Movements

Figure 3
AJ with Exercise Movements

Figure 4
Malcom with Fighting Movements

Figure 5
Granny with Dance Movements

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Figure 6Scarlet with Zombie Fighting

Implementation and Evaluation
The VR Trainers developed were compiled into the app, ready to be used by the students. The
developed app was published into a .apk file and linked to a Head Mounted Device (HMD).
The facilitators involved in collecting data were trained to use the HMD and run the app. The
participants for the evaluation were chosen from a special needs secondary school in Malaysia.
The teacher selected the students to join the study who could understand the information andanswer all the questions. The ethical committee in the university approved the study.
Permission to conduct the sessions was also obtained from the Malaysian Special School
Department.
Twelve students were interviewed after they were introduced to the virtual trainers to learn thephysical exercise movements. They were aged between 16 to 17 years old and have learning
disabilities. Before the exercise started, students’ physical fitness was assessed. The students
were divided into two groups of six students each. The first group had to follow intense andrigorous exercises with AJ, Malcolm, and Scarlet. The second group required low-intensity
movement. Therefore, Granny and Shaky were selected for this group. Both groups wereassessed separately inside a gym in one of the educational institutions in Malaysia. They wereasked to complete a simple warm-up first and then follow the trainers’ movement with afacilitator’s help. After the session ended, each participant was interviewed.
Overall, all the participants were able to follow the movements of the trainers. Students who
participated in this study also liked the music used in training. They could remember andclearly describe the song used in the activities. However, some students preferred trendier
pieces that suited their moods. Some students even expressed enjoyment and excitement indoing a low-intensity salsa and dancing activity. They said the virtual fitness trainers wereattractive and willingly followed the movements without coercion. The respondents alsosuggested adding more trainers to increase their fitness in the long run.
Most said it was 3D and realistically done for all the virtual trainers. A majority said they found
Scarlet’s movements a bit weird but that it has a value that added to the learning experiences.
According to the comments, Scarlet shows specific signs like jumping and running around thatprovide more student fitness activities. For the combat trainer (Malcolm), students stated thatthey could learn and hopefully increase their fitness. Most of the students could describe indetail the movements they had learned. They even mentioned “looking around” by one of the
trainers to prove that they could remember all the movements. The following are selected
quotes from the interviews.

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Group 1: Virtual fitness trainers with High-Intensity Movements
Respondent 1:“The movement is ok. The Scarlet has specific movement and added new fun
experience”“The movement of Malcolm exposed to fitness activities and fitness”
Respondent 2:“I have done most of the activities previously, but it is ok”
Respondent 3:“It is too simple for me. The song should be changed to something moretrendy”
Respondent 4:“The movement is not too hard. Easy for me to follow”
Respondent 5:“The zombie movement is unique. I like it because it is different from the otherapp I used before because the trainer got movements”
Respondent 6:“I like it. I sweat a lot”
Group 2: Virtual fitness trainers with Low Intensity Movements
Respondent 1:“The Sharky training is OK”
Respondent 2:“Sharky movement is best. It got viral song. But I don’t prefer the Salsa
movements. It is slower than Sharky”
Respondent 3:“Should be more movements for Sharky. It is good. “
Respondent 4:“This make me sweat a lot.”Respondent 5:“I like the music”
Respondent 6:“I feel good”
We concluded from the interview that all students prefer Sharky or Scarlet’s movements. Thisis because the actions of the two virtual trainers were easy to understand and follow by thestudents. The comments and suggestions from the students were used as a guideline toconstruct the AR book. However, for the first edition of the AR Book, the trainer characters
were not included in the AR Book because we had to follow the requirements of the original
textbook.
Phase 2: The Development of an AR Book
Needs Analysis
The design at this stage was based on semi-structured interviews done to know how students
responded to the lessons with virtual trainers and secondary data concerning the appropriate
design of physical exercise movements collected from the textbooks and teaching materials
from the Department of Special Education, Malaysia. Additional information was also

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collected from reports and research papers for the second phase. Based on the feedback from
Phase 1 and the information collected in Phase 2, AR books and an AR app were developed tofacilitate physical education for students with disabilities.
Design and Development
Selecting the best design for an AR trainer suitable for physical exercise involved analyzing
the information from official sources such as textbooks as well as using the CTML principles.
The content covered was as follows: (a) Stretching, (b) Muscle Strength, (c) Parcourse
Training, and (d) Fitness Test. All movements were designed based on the guidelines from theofficial textbook the Ministry of Education provided.
The parcourse activity was specially designed for students with a learning disability. Parcourse
is a set of training that often gives a well-rounded workout based on health-related fitness
concepts (MacDonald et al., 2017). This is a fitness activity with checkpoints designed toincrease the students’ fitness level and be suitable for special needs students (Mustaffa et al.,2019). Thus, the activity was chosen to be included in this study. The training was done byreferring to the Physical Education textbook published by the Department of Special education
with the title, “Pendidikan Jasmani dan Pendidikan Kesihatan Pendidikan Khas Tingkatan 4,”translated as “Health and Physical Education for Form Four Special Needs Students.” Figure
7 displays the screenshot of the parcourse training. The textbook covers one year of the syllabus
for the secondary form four students in the Malaysian Special Education school system. In the
textbook, students are provided instructions on fitness activities to do in class for PE.
Figure 7
The Parcourse Training in Special Education PE textbook (Mustaffa et al., 2019)

Based on the interview data, the authors concluded that the activities must be increased andfollow a particular order. According to the students, good fitness trainers must have a specifictraining routine to engage users and improve fitness. For a virtual trainer to gain the users’attention, it must be designed in 3D and provide a good animation design. They like vigorous
activities and a human-like trainer. Therefore, for the AR book design, the training started withstretches, followed by particular fitness activities that target muscle areas. The students also

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stated that they could understand and follow the routine with repetitive movements. These
items were added to the design for the AR book.
The analysis of secondary data such as reference books and formal guidelines from the Ministryof Education was the basis for creating the AR book and the AR trainer. AR is relayed to thestudents using AR-printed books and apps downloaded from the app store. The book has eightpages, excluding the cover and end page. There are two methods of learning with this book.
The student can just read and follow the steps illustrated or use the AR app to view the animatedversion of the activities. Table 3 describes the sources and how they were applied in the designof the AR book.
Table 3The Resources for the Design of the AR Book
Item

SourceSpecial Need School
Textbook

Health and Physical Education forForm Four Special Needs Students(Mustaffa et al., 2019)

Immersive Multimedia Immersion Principle in Multimedia
Design Principles
Learning (Makransky & Mayer,
2022)
Suggestions from
Reports on PhysicalExercises for SLD

The resources applied in
the design
The design of the parcourse
training in the book wasused as a guide for thefitness course training in
the AR book
The application of CTML
for immersive learning

Activities suitable to engage SLD in At-home activities for SLD
PE (McMahon et al., 2020)
How SLD learn (Hardiyanti &Azizah, 2019)

With the AR book, the students will have to learn the physical exercises, starting withstretching. The first activity stretches the Stemocleidomstoid, Pectoralis Major, Deltoid,
External Oblique, and Latissimus Dorsi muscles. The students have to perform neck, shoulderside, and hamstring stretching. The next part is focused specifically on muscle endurance withmore intense activities. The students must perform push-ups, sit-ups, bench-ups and down,squat, and jump. The targeted muscles are Pectoralis Major, Biceps, Triceps, Abdominal
Muscle, Quadriceps, Hamstring, Gluteus Maximus, and Gastrocnemius. The last activity is thefitness test compulsory for most sports activities. In this test, the students will have to doshoulder rotation, body curling, side bend, giant steps, and back legs stretching. Table 4
describes the students’ activities and the target output. The book used the Malay language as itis the native language of the students and the original textbook also used Malay. Figure 8
displays a page from the book that functions as a marker where the students can use theirsmartphones to view the AR overlay of activities. Figure 9 shows the animation overlay when
the camera from the student’s smartphone hits the marker.

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Figure 8
The Page from the AR Book

Figure 9
The AR Overlay

Table 4The Activities, Motions Illustrated, and Target Outcome
Activities
StretchingMuscleEndurance
Fitness Test

Motions Illustrated
Neck Stretching
ShoulderSide Stretching
HamstringNon-stop Push up,
Sit-upBench up and Down
Squat and Jump
Shoulder Rotation, Body
Curling, Side Bend, Big Steps,
Back Legs Stretching

Target outcome
Stemocleidomstoid
Pectoralis Major, Deltoid
External Oblique, Latissimus Dorsi
Hamstring, Gluteus maximus
Pectoralis Major, Biceps, Triceps
Abdominal Muscle
Quadriceps, Hamstring
Gluteus Maximus, Gastrocnemius
To measure fitness by measuring
the frequency, intensity, and timefor each activity

Discussion
Optimal Motor Learning Theory states that motivation and attention are essential to improve
motor performance and achieve targeted motor learning. When educators and learning material

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developers consider an appropriate design for physical exercise education, the student’s
motivation is essential. Motivation is the contributing factor that makes the students keep doing
the exercise until specific targets are achieved. The students will be engaged and active inphysical activities when the learning materials can motivate them to do the activities (Mokmin& Jamiat, 2021). According to this theory, for motivation, students must be given autonomy inlearning control, and eventually they can focus on their goals. We found that motor learning
can be enhanced by allowing learners to control specific aspects of exercises or assistive
devices. During the movement restriction times, such as the pandemic, when the students muststay mostly at home, they must be motivated to follow through with the training to maintain acertain level of activity and physical fitness.
This study demonstrated how observational learning, even in a virtual context, is beneficial forphysical education. One of the most prevalent instructional approaches for motor learning
acquisition is demonstration or observation (D’Innocenzo et al., 2016). Thus, in the first phaseof this study, five types of virtual trainers were introduced to allow the students choice of the
trainer(s) they wanted to learn the exercise from and facilitate their learning. They could also
freely rewind and pause whenever needed throughout the lessons. Through the interviews,
students indicated they were satisfied with the activities they had completed and could easily
navigate among the trainers and explore which activities they wanted to do. The trainers’ catchy
sounds and different appearances also motivated and attracted the learners to move along. In
this study, the virtual trainers were set in various settings with different levels of exercise to
keep the students engaged and motivated when doing the movements. According to Andrieux
and Proteau (2016), when several skill levels are imposed, the students’ learning throughobservation also improves. Here, technology helped the trainer to teach remotely with a virtual
trainer. Although an in-person trainer may be the best choice, these virtual trainers are practicalwhen remote learning with technology is needed. Nevertheless, for an efficient PE app, more
activities should be added in the future.
AR is the next emerging technology after VR for instructional content designers due to its
effectiveness in providing immersive learning (Ariffin et al., 2022) and ease of access. In theera of disrupted education, where face-to-face learning in classroom settings is sometimes
impossible to implement, some schools have to opt for immersive technologies. Various studies
have shown that learning becomes more exciting by implementing AR technology for physicaleducation, leading to more positive learning outcomes. Chen et al. (2020) have used AR
technology to demonstrate Tai-Chi movements to older adults. They have developed an app
that applied augmented reality-assisted training with selected Tai-Chi movements tailored tothe practitioner’s ability. The results showed that the users successfully learned the fitness
exercise and got positive outcomes in their activities.
In this study, the AR has been designed according to the specific guidelines from the physicaleducation textbook. The activities started with stretching that targeted specific muscle points,
which the AR technology illustrated immersively. The other activities in the book are muscle
strength and fitness training, which are also immersively taught to the students. For students
with disabilities, AR is an excellent potential tool, as suggested by learning material developers
as it can facilitate the unique needs of special needs students.
The best strategy for PE in current distance home learning is to supplement the traditional
teaching with creatively designed and personalized resources based on the students’ needs (Roeet al., 2021). Both VR and AR technologies used in this study can help developers andeducators facilitate the teaching and learning process of PE.

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Conclusion
This study examined how students can learn physical education via virtual trainers and themost effective design of a virtual trainer from the students’ perspective. However, the virtual
trainer and the AR book have not been tested for different types of disabilities, and more virtual
trainers should be added to improve the app. The results, alongside secondary data information,
were used to design and develop an AR book with an AR app as a learning material for students
with learning disabilities. Since the book is developed for the usage of the students with special
needs, the five virtual trainers are not included in the book’s first publication. It is possible to
be included for the subsequent publication of the book for public use. The results show thatimmersive technology designed using the Optimal Motor Learning Theory and carefully
implementing the Cognitive Theory of Multimedia Learning can be a suitable learning material
for PE and should be considered for inclusion in the learning material design for students withdisabilities. For future study, we suggest the book and the trainer be tested with different groupsof disabilities and more movements added for the virtual trainers.
Acknowledgements
This work was supported by the Ministry of Higher Education Malaysia for the Fundamental
Research Grant Scheme with Project Code: FRGS/1/2021/SSI0/USM/02/10

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McMahon, D. D., Barrio, B., McMahon, A. K., Tutt, K., & Firestone, J. (2020). Virtual
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Corresponding author: Nur Azlina Mohamed Mokmin
Email: nurazlina@usm.my

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Using Educational Digital Storytelling to Enhance Multilingual Students’ Writing Skills
in Higher Education
Eleni Meletiadou
London Metropolitan University, UK

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Abstract
Educational Digital Storytelling (EDS) is a powerful technology-enhanced learning approachthat enables learners to develop 21st century skills. Students often complain about their digital
illiteracy, academic writing challenges, and lack of interaction in classes. In the current casestudy, 50 multilingual and multicultural ESL student-teachers were involved in the creation ofdigital stories in an undergraduate module. Students used Canva to create their own digitalstories which included text, images, videos, podcasts, and infographics. The overall aim wasto improve their academic performance and motivation towards learning, and explore theirattitudes towards this new dynamic learning-oriented instructional strategy. The current semiexperimental study tried to challenge monolingual bias and promote a more integrated andinclusive approach to learning as both efficient and desirable in preparing university students
for constructive involvement with various cultural perspectives promoting the creation of
global networks. Findings from both quantitative (paired and independent samples t-tests oftest scores) and qualitative (anonymous feedback, lecturer observations and focus group
discussions) data analyses indicated that learners improved their writing performance, criticalthinking skills, self-confidence, and intercultural awareness. Students stated that they felt proudof their final products (digital stories) and that the EDS intervention was rewarding. However,some non-traditional students reported facing challenges with the use of technology. Learners
expressed their wish to experiment more with digital stories and use them in other modules.
Implications for teaching and learning practices in the Higher Education academic environmentand suggestions for the successful implementation of EDS at the tertiary level are provided.
Keywords: educational digital storytelling, digital skills, writing performance, motivation,
multilingualism, undergraduate students

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Educational Digital Storytelling (EDS) is a pioneering learning-oriented technology-enhanced
approach which allows students to develop a wide range of academic and professional skills(Dewi et al., 2019; Hava, 2021; Robin, 2008). Lambert (2012) describes a digital story (DS) as
a narrative blending visual and aural features for telling a personal story. Various studies haveindicated that EDS provides numerous benefits to learners. Selective examples include: (a)affective—enhancing motivation (Hung et al., 2012), (b) cognitive—developing criticalthinking skills (Yang & Wu, 2012), (c) academic—developing writing skills (Yildiz Durak,
2018), (d) technological—enhancing IT skills (Chan et al., 2017), and (e) social—promotinginteraction (Lin et al., 2013). Additionally, Sukovic (2014) indicated that EDS provided anincreased sense of achievement to learners while Hung et al. (2012) underlined thedevelopment of student confidence. EDS also supported a conducive context for theenhancement of language skills (e.g., listening and speaking) and mastery of story structure
and multimodal expression (Liu et al., 2018). Finally, EDS was reported to increase otherawareness as creating, crafting, and exchanging stories are part of people’s everyday life and abasic element of social group interaction (Carmona & Luschen, 2014).
This article examines the use of EDS in a Higher Education Institution (HEI) in Cyprus. The
overall aim was to improve multilingual and multicultural undergraduate students’ writing
skills and motivation towards learning. International ESL student-teachers were asked to telltheir own stories using Canva and a variety of resources, exchange ideas, and provide feedback
and suggestions for improvement to each other using the target language (English) and theirfirst language (L1). This was the first time these undergraduate students were asked to engage
in this creative activity.
This intervention enabled undergraduate student-teachers to craft and share digital stories (DS)with the aim of stimulating creative and thoughtful philosophies of teaching to develop specificskills. The research considered that students might enjoy using media to describe their teaching
aspirations in a narrative format. Robin (2008) proposed that EDS could be used to synthesize
learners’ knowledge and findings in a more imaginative, meaningful, and interesting way.
While reflecting on their current teaching principles and practices, these learners wereencouraged to refine their teaching philosophy so as to better detect and manage unpredictedchallenges. IT use was necessary during the development of their DS. This reflected the current
need for educators to use technology in their sessions due to crises such as the COVID-19
pandemic (Ahmed & Opoku, 2022). As soon as the pandemic started, educational institutions
had to resort to blended and online learning worldwide (Choi et al., 2021). Consequently, theexperience and the findings of the current study should encourage student-teachers to use EDS
in their classrooms in the future. Scant research has investigated learner perceptions of EDS
use in a Higher Education (HE) instructional context including the use of EDS to develop
undergraduate student writing skills (Eissa, 2019). In the current research study, learnerperceptions about EDS were explored as they participated in an EDS intervention. The aim was
to examine and understand student reflections regarding EDS use for educational purposes.
The research questions for the study were:••

What was the impact of EDS on undergraduate multilingual student-teacher writing
performance?
What were undergraduate multilingual student-teacher perceptions of the impact of
EDS on their motivation and academic achievement?

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Literature Review
EDS allows multilingual and multicultural learners to explore the possibility of using
multimodality to create and share their stories with their peers and even with a wider audience(Lambert, 2012; Liontas & Mannion, 2021). Students can combine their written texts withpictures and music or video to participate in new forms of online literacy, become morecreative, and understand how these forms may relate to print literacy they use in their everyday
lives as students. However, many lecturers avoid using EDS in their classrooms due to theamount of time students need to complete relevant tasks, the demand on IT equipment andexpertise, and doubts regarding value as EDS does not conform to the rules of traditional
writing assignments (Belcher, 2017).
Nevertheless, such new approaches to writing allow learners to explore basic academic writing
concepts from a different perspective (Bloch, 2018). EDS encourages the use of a mixture ofdifferent modes of print, visual, and aural expressions which allow students to be more creativeand engage more deeply in their own learning process. Students can therefore make choices
and transform staid assignments into meaningful exploration for alternative expressions. A DS
allows learners to present their own ideas by telling their own story, for example, relating it toa significant period in their lives. They can also record parts of their stories using their own
voice or add voices or videos of other people. They can include images, photos, infographics,
or even music. These multimodal forms have become very prominent as students can tell theirown story in a unique way. DS also became more important when used outside the teachingsessions for professional reasons, e.g., to showcase their creative talents to potential employers(Yancey, 2004).
Engaging learners in comprehending genre (stories) across both print and digital modalities
potentially allows learners to become more active members of their academic and professionalgroups as well as the wider digital world. Additionally, the implementation of EDS may
facilitate discussions on language and content in the writing classroom as students may findthe creation of DS preferable to writing an assignment. Students can also use their own native
language, what Wei (2018) refers to as “translanguaging” – a practice used to create
intercultural narratives and improve multi-cultural awareness promoting a “circle of digitalstorytelling” (Pahl & Rowsell, 2019, p. 12). Unfortunately, opportunities for learners to engage
in activities which involve the utilization of more than one language are infrequent in HEIs in
Cyprus and other European countries.
Silvers and Shorey (2012) discussed learner identities and the significance of new literacies
allowing students to become socially responsible, critically literate, as well as genuinely
involved in the learning process. Barrett (2019) claimed that EDS combines four studentcentered learning techniques: learner engagement, reflection for enhanced learning, projectbased learning, and the successful blending of technology with instruction. McLellan (2008)
claimed that EDS facilitates training in creativity, complex problem solving, cooperation, andautonomy. EDS may also promote reflection and collaboration (Jamissen et al., 2017). Other
benefits include flexible pacing, increased concentration, imagination, extended reach,motivation, and cooperation (Robin, 2016). Some researchers also claimed that users’ creationof a personal and academic identity, the development of skills such as digital searching andwriting literacy, and its interdisciplinary and participatory nature, were advantages forengaging students in this innovative approach (Chiang, 2020; Özüdoğru & Çakir, 2020).

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Taking into consideration increased digitization of education due to COVID-19 (Díez Gutiérrez
& Gajardo Espinoza, 2020; Wang et al., 2020), there is an increasing need to promote social
justice and support all students, irrespective of their background, especially in providing
training and technical support (Choi et al., 2020; Portillo et al., 2020). Heidari et al. (2021)
highlighted the significance of digital informal learning for students’ academic development.
The current pandemic forced HEIs to prioritize digital technologies (Murphy, 2020) althoughthese presented a number of challenges to both students and academics (Toquero, 2020). To
ensure students' academic engagement, HEIs needed to develop students’ competencies(Villela et al., 2020) through involvement in innovative learning methods such as EDS (Wu &Chen, 2020).
Although some studies have explored EDS in primary (Fokides, 2016; Liu et al., 2018) andsecondary education (Damavandi et al., 2018), few studies have investigated EDS in
foreign/second language education (Eissa, 2019). Moreover, limited studies have examined theimpact of EDS on learner writing skills (Balaman, 2018) or motivation (Hava, 2019) in Higher
Education (HE). For example, Balaman (2018) explored the impact of EDS on 43 English as a
Foreign Language (EFL) English-major student writing skills at the School of Foreign
Languages, Cumhuriyet University for 14 weeks. Hava (2019), who examined the effects of
EDS on student motivation in teacher education, reported that learners’ self-reliance andpersonal use improved after their engagement in EDS activities. The research highlighted that
EDS is an emerging approach for the enhancement of writing skills in the narrative genre, butmore research needs to be conducted to explore its effectiveness when used with other literary
genres in different contexts.
In this article, the challenges of using EDS to assist students developing writing skills andincreasing motivation was explored. EDS was introduced in an undergraduate course at a
University in Cyprus. The aim was to help learners improve essay writing skills. As some ofthese students seemed to struggle with academic genres, a goal of the research was to use EDS
to introduce students to key concepts from a different perspective (Bloch, 2018). The following
section lays out the research methods and describes the implementation in detail.
Methodology
This research employed a mixed-methods approach (Almeida, 2018), collecting bothquantitative (essay scores) and qualitative (anonymous student feedback, focus group
discussions, lecturer’s observations) data to answer the research questions (Table 1). Paired ttests compared students’ scores in the pre- and post-tests for two modules (control andexperimental). Student performance in the control versus the experimental module were also
compared to examine the impact EDS had on academic achievement. Finally, thematic analysis
was used to analyse data from student feedback, triangulating data with lecturer observations
and comparing main themes for increased reliability and validity (Flick, 2018).

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Table 1
Methodology
Mixed-methods approach
Quantitative data

Qualitative data

Sample: 50 students
Sources of Data:
1. In-class pre- (week 1) and posttests (week 13)
2. Two essays about students’personal teaching philosophy

Sample: 50 students and 1 lecturer
Sources of Data:
1. Anonymous peer feedback via
Mentimeter every 3 weeks
2. Focus group discussions every 2
weeks
3. Lecturer observations about theprocedure and student responses
to the implementation, every
week

Method of analysis: Paired andindependent sample t-tests
Raters: Researcher and an assistantMethod of analysis: Thematic analysisof recorded and transcribed material.
Triangulation with data fromobservations.
Coders: Researcher and an assistantFifty EFL/ESL student-teachers (Table 2) attended two mandatory parallel modules on Lesson
Planning and Classroom Management which explored basic principles and approaches ofteaching and learning as part of their regular undergraduate curriculum. One module focusedon productive skills (writing and speaking) and the other on receptive skills (listening andreading).
The researcher received research ethics approval for the study and students provided informed
consent. The researcher maintained confidentiality of learner responses. The DS and
Mentimeter feedback were not compulsory and did not count for student grades.
Table 2Student Demographic Characteristics
Students

Frequency

Male

19

GenderFemale

31

Age

22-30

29

30-40

21

High-performing learners

4

Average-performing learners

10

Low-performing learners

36

Class rank

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The Implementation Process
In previous years, many students who completed the same two modules complained about their
low writing performance and the repetition of monotonous essay assignments used to assess
their academic performance. Therefore, DS was implemented instead of written assignments
in one of the two modules and its impact explored. All students took a pre-test (Figure 1), which
was an essay to assess their writing performance at the beginning of the academic semester.
The essay asked them to discuss their current philosophy in teaching productive and receptive
skills. The researcher implemented EDS in the module focusing on productive skills(experimental module) and used the module focusing on receptive skills as the control module(no use of EDS). The aim was to explore the impact of EDS on student writing skills andmotivation. The post-tests for the experimental and control modules were the final assignments
students had to submit at the end of the semester.
Figure 1
EDS Implementation Scheme
Pre-test (essay) Week 1 (samefor bothmodules)Peer andtutor
feedback
for bothmodules

First draft of theindividual DS(experimental module)vs written assignment(control module)

Instructionofeducational
theories

Final draft of theDS (experimental
module) vs written
assignment (control
module)Instructionofeducational
theories

Preparation andsubmission of the
final assignment(essay) for both
modules (post-test)
Participants in the experimental module were asked to prepare a DS by the end of the module.
Students did not receive a grade for their DS, but they had to use the texts they produced intheir DS for their final assignment. They were expected to work individually, then share theirstories with peers and receive feedback from their lecturer and peers.
During the implementation, student communication in their L1 was promoted in class tofacilitate the flow of ideas and allow a fruitful collaboration as students were asked to help andlearn from each other. Learners were encouraged to create and share their multilingual DS tocross boundaries of language, culture, and the syllabus using the extended possibilities forcooperation and communication which were made possible by the digital media. They were
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multilingualism was used with the aim of developing multiliteracies and involving the many
voices of the students. A significant part of this multilingual process took into consideration
learner opinions and examined multiple perspectives as a predominant aspect of diversity andsocial inclusion. Learners were also free to share their DS on YouTube to reach a wider
audience. At the beginning, students were asked to create a first draft. They received feedback
from their peers and lecturer and then completed their final draft. The students in the control
group went through the same procedure (including the peer and tutor feedback stage with afocus on translanguaging) but produced written essays instead of a DS.
All learners had to participate in weekly webinars (3 hours each) and a forum every week forboth modules. In terms of written assignment, the lecturer requested learners to discuss theirpersonal philosophy in teaching (to develop students’ writing skills in the experimental moduleand their reading skills in the control module). They were also asked to prepare a DS to present
their philosophy using Canva in the experimental module. They used covered theories in themodule and developed digital learning skills at the same time. This allowed them to include
not just text but also images, infographics, podcasts, and videos to present their stories. Theirgoal was to illustrate ways in which they would teach specific skills successfully and manage
their classrooms effectively. Students also spent time discussing the theories in depth andwriting drafts of their essay in the control module but did not prepare a DS. The lecturer andthe assistant used the same assessment criteria to assess all student essays.
Students were divided into groups and participated in discussions regarding their DS(experimental module). The researcher used various questions to prompt discussion in the focus
groups every 2 weeks. For example, did you find this experience (using DS) worthwhile? Whyor why not? Every 3 weeks, learners also provided anonymous feedback using Mentimeter(https://www.mentimeter.com/), an interactive digital platform used by educators to elicit
anonymous responses from students, to explore their attitudes towards the learning strategy.
The researcher kept a research diary during the implementation of this technology-enhanced
approach, adding field notes every week. Students also had access to a Google document where
they shared thoughts about participating in the implementation. This allowed the lecturer tointervene and provide support when needed and to learn which teaching aspects were helpful
during the EDS implementation. The lecturer/researcher took an insider role and devoted timelecturing, observing, and collecting data. Finally, peer assistance and feedback was sought
during the study (Morse, 2016).
Findings
Impact of EDS on Student Writing Performance
The researcher compared learner writing performance on a pre- and a post-test (Table 3). A
second assessor marked 20% of the tests. Both received inter-rater training in which theymarked 3 scripts (a high quality, an average, and a low quality one) utilizing a rubric provided
by the University. The second rater was a colleague who taught the same module to anothergroup of students. Inter-rater reliability was estimated by calculating the similarity percentageof 94 %. The high percentage indicated student grading was reliable (Chaturvedi & Shweta,2015).

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Table 3Student Writing Performance
Measurement

N

M

SD

Pre-test same for 50
both modules

25.9

17.4

Post-test with EDS 50

52.7

Post-test without
EDS

33.4

50

T value Df

Sig.

Cohen’s d

14.5

17.4

p < .05

d = 1.67

17.9

13.4

49

d = .42

A paired t-test was used to explore academic progress in the two modules. The test indicated astatistically significant difference between learner pre-tests (M = 25.9, SD = 17.35, n = 50) andpost-tests (M = 52.72, SD = 14.53, n = 50) in terms of their writing performance (t (49) = 17.35,
p < .05) for the experimental module, while there was a small difference for the control module(M=33.4, SD=17.9, n=50, t(29)=13.4, p<.05). Cohen’s effect size value (d= 1.67) suggested a“large” effect size and high practical significance for the impact of EDS on learner writing
achievement for the experimental module and a small effect size (d=.42) for the control
module. These tests indicated the use of EDS improved writing performance in essay writing
compared to the written assignments.
An independent t-test was also used to examine the differences between the post-test scores ofthe same group in the two modules. On average, student scores in the EDS module (M= 53.2,
SD=13) were higher than those in the module without EDS (M= 33.4, SD=18). This difference
was statistically significant t (49) = 6.33, p = .000, d = 1.27. This finding highlighted that theuse of EDS had a statistically significant impact on student writing performance confirming
previous research (Yildiz Durak, 2018).
Paired t-tests were also utilized to explore the influence of EDS on learner writing performance
taking into consideration each one of the categories in the marking rubric. There were
statistically significant differences between learner pre-tests and post-tests on the fivecategories of the marking rubric which showed the various areas of learner writing achievementin the EDS module. Overall, EDS had a significant impact on all aspects of learner academic
achievement. Cohen’s effect size values suggested a “large” effect size and high practicalimportance for its impact on various aspects of student writing achievement (Table 4), but
predominantly for vocabulary and language use confirming previous research (Duman &Göcen, 2015).

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Table 4
Aspects of Writing Performance
Measure
ment

N

M

SD

‘T’
value

Df

Sig.

Cohen’sdContent/

Pre-test

50

5.7

4.3

9.31

49

p < .05

Analysis

Post-test

12.3

4

Cohen’s
d=1.59Organisation/

Pre-test

5.5

4.6

8.23

49

p < .05

Structure

Post-test

10.9

4.3

Cohen’sd=1.21

5.7

4.8

10.93

49

p < .05

11.9

4.8

Cohen’sd=1.28

5.4

4.4

6.21

49

p < .05

9.6

4.7

Cohen’s
d=.9

3.8

3.1

7.2

49

p < .05

7.9

3.4

Cohen’s
d=1.2

50

Vocabulary & Pre-test
Language use
Post-test

50

Purpose/Pre-test

50

Audience

Post-test

Mechanics & Pre-test
Presentation

50

Impact of EDS on Student Motivation and Academic Achievement
The research used thematic analysis to explore the perceived impact of EDS on learneracademic achievement and motivation. Analysis of qualitative data relied on an inductive
approach, intended to identify the main themes and patterns emerging in the data (Lester et al.,2020). Finally, interpretive content and thematic analysis were also performed (Creswell &Poth, 2016).
The researcher collected qualitative data from learners (i.e., anonymous feedback and focus
group discussions). The data were first read in their entirety and then coded using an
exploratory and holistic approach (Saldaña, 2021). Saldaña’s (2021) method of “in-vivo”
coding (referring to direct learner quotes from student anonymous feedback and the focus
group discussions) was applied to the data. Analysis of the data showed different themes, e.g.,development of critical thinking skills and self-confidence (Table 5). Data were thentriangulated with lecturer field notes. An additional lecturer reviewed 20% of the data collected
and analysed it to minimize researcher bias (Cumming et al., 2006).

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Table 5
Impact of EDS on Student Motivation
Themes (withfrequency)
Positive Impact
1. Digital skills
enhancement (48)Sample Student Comments

2. Development ofcritical thinking,reflective andwriting skills (31)

I was able to develop my writing skills as creating an EDS was sointeresting…I got plenty of support, too…I was also able todevelop my critical thinking skills, reflect on my work andprovide arguments to support my ideas and suggestions…allthese theories suddenly made sense… (Anonymous feedback –session 3)

3. Professional skills
enhancement (37)

EDS allows me to prepare a portfolio of tasks I can use to
showcase my work to prospective employers. It also allows me touse interactive elements which make my work even moreengaging for my audience. (Focus group discussion 3)

4. Impact on
students’enjoyment oflearning (46)

I love using EDS. I can tell my own stories and be creative. I
want to use it in other modules, too. I will certainly use it with mystudents as well. Writing seems so much fun now… (Anonymous
feedback – session 4)

5. Influence on
students’ selfconfidence (45)

I felt so proud of myself as peers congratulated me on thestrategies, I have used to cope with various challenges I faced
while creating EDS… (Focus group discussion 4)

6. Impact on
development ofinterculturalawareness (41) &collaborative skills(35)

Interacting with peers from around the world through our DS andsharing problems and experiences helped us realise we are notalone and that peers face similar problems in othercountries…We would not reach other people by just writing an
essay as part of our assessment for the module… (Anonymous
feedback – session 4)

7. Improvement oflearning attitude(47)

Working on real-life stories and personal experiences made us
more conscious of potential challenges we might encounter asteachers and some of the pitfalls of our profession. We becameaware of different scenarios, and stories were more engaging as
we could listen to podcasts or short videos and read our peers’
texts…I do not think I would have paid so much attention if I just
had to read a plain assignment… (Focus group discussion 5)

I must confess I was a bit terrified when I heard that we had toprepare a digital story as I am not that good with computers. Ourlecturer was very supportive. Incorporating images and videos
was very exciting and I feel so confident now… (Focus group
discussion 2)

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Challenges
8. Non-traditional
students’challenges withtechnology (17)

I cannot complete my assignment… I do not know how to use
podcasts. Thank God, one of my peers helped me. We needadditional support. For example, an assistant who could devote
some time to each one of us individually to help us…We are
old…We did not use these techniques 10 years ago… (Focus
group discussion 1)

9. Lack of stable
Wi-Fi and accessto the internet(10)

Wi-Fi is very poor in the block of flats I live… Lots of people log
in at the same time and I cannot download the images I
need…Otherwise, I have to wait forever…Who will help me andhow? (Anonymous feedback - session 2)

10. Lack ofequipment, i.e.,
personal laptop(4)

I only have a desktop computer which is rather slow. I need help
to make changes so that I can improve my stories… They are sopoor. I need training as well. (Anonymous feedback - session 1)

Data indicated participants valued authentic engagement in highly interactive real-life
experiences, such as EDS, which allowed them to express unique points of view using words
from their own language. Students indicated that EDS enhanced digital, professional, andcritical thinking skills; improved writing skills; increased motivation and self-confidence; anddeveloped intercultural awareness and collaborative skills compared to written assignments(Table 5). These findings, supported by lecturer observations, corroborated previous researchin other settings (Hung et al., 2012). As students had to submit similar written assignments for
all their modules, which they characterized as demotivating in previous surveys, they weremore excited when working on their DS. They collaborated more with their peers, developed
digital skills, and resolved various problems encountered while preparing their DS. Although
there were twenty-one non-traditional students who faced several challenges while using Canva
and other technology, all students successfully submitted their digital stories. Students shared
their final products through social media, and some of them reported they intended to include
them in blogs and share them with friends and colleagues. Student-teachers felt less isolated
and enjoyed sharing DS with peers from around the world when their identities were reflectedin the DS.
However, some non-traditional students reported that they faced challenges while using Canva,
needed additional training to use certain elements, such as podcasts, and more time to prepare
a DS compared to written assignments. These findings were also confirmed by the lecturer who
observed that students faced a number of challenges due to lack of internet at their home. The
lecturer confirmed most of the benefits and challenges except for the lack of laptops as most
students seemed to have one. In sum, the current implementation may help educators reflect on
their practices, help students develop digital skills, and inspire HEIs to create moreinterculturally oriented curricula.

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Discussion
Involving and motivating learners is crucial for effective learning and educators should prepare
meaningful activities to increase learner interest and engagement and foster active learning(Hyun et al., 2017). Previous studies indicated that the use of technology enhances student
writing performance and attitudes towards learning (Barrot, 2021). According to the qualitative
findings of the current study, EDS promoted authentic learning as students often used their own
personal stories, which they integrated with theory, creating compelling content (Table 5,Comment 7). This was not always possible when students were asked to write a traditional
essay. Completing challenging tasks successfully as they developed digital skills due to the
EDS increased students’ sense of autonomy. Students learned how to create podcasts, weremore self-confident and creative, and improved their overall attitude towards learning (Table
5, Comment 7). Consequently, the study indicated that EDS emphasized various motivational
constructs such as task significance, self-reliance, and reflective practice, confirming previous
research (Jamissen et al., 2017; McLellan, 2008).
The use of technology for EDS created meaningful, interactive, and inclusive multimodal
instructional content (Rubino et al., 2018) that facilitated the creation of a community oflearning (Table 5, Comment 6). As undergraduate students often have varying life experiences
and sociocultural and linguistic backgrounds, the EDS intervention encouraged negotiation of
new theories and knowledge allowing learners to present their own perspectives and negotiate
language teaching with peers. This ultimately enhanced students’ professional and academic
skills (Tables 3 and 4), especially their writing skills (Table 3), confirming previous research(Rubino et al., 2018). Moreover, the current study indicated that EDS could improve student
academic performance significantly in terms of writing, improving student vocabulary andlanguage use, content, and organization as well as their sense of purpose and audience (Table4). This outcome was also consistent with previous research (Rong & Noor, 2019; Wu & Chen,
2020; Yamaç & Ulusoy, 2015) which indicated that EDS was a viable approach to help students
improve various aspects of writing.
EDS provided an authentic context allowing students to work on real-life tasks (Table 5,Comments 6 and 7). This motivated learners and inspired them to put additional effort toimprove their academic performance as they found EDS tasks more enjoyable. Furthermore,as a multimodal writing tool, EDS allowed students to use a variety of elements, such as
infographics and videos, in addition to written text. Thus, they could convey their intended
message and present the associated theories much more vividly and powerfully. The lecturer
observed that students’ DS were an interesting marriage of narrative, text, and technology.
Rossiter and Garcia (2010) referred to such use of DS as “a potent force in educational practice”(p. 37).
In addition, EDS allowed students to develop their writing skills more effectively due to theaudience effect. Having an audience urged learners to pay more attention to detail and try evenharder to improve their texts. The findings also supported the idea that involving a real audiencecreated a genuine language context (Table 5, Comment 5). The technological advances indigital media offered innovative ways of exchanging stories and enhancing interculturalawareness and communication. In a fast-paced networked society, there is an even greater
demand to enhance intercultural competencies and multilingual skills. These skills can increase
the overall set of 21st century competencies frequently linked with professional skills neededin the global workplace, e.g., creativity, imagination, cooperation, problem-solving, and digital

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and multimedia literacies. Considering lecturer observations, these were some of the skills thestudents developed when they were involved in EDS.
EDS also enhanced student motivation and engagement. Literature indicated this often led toincreased writing performance (LoBello, 2015). EDS places emphasis on the process and thefinal product, making learners more persistent and allowing them to engage more fully withthe writing process, as they tended to invest considerable time and effort before asking for
feedback from their audience (peers and lecturer). To prepare their accompanying videos andpodcasts, students had to re-visit their work. This allowed them to detect flaws in their writing
compared to if they had to produce written text only as was the case for their traditional final
assignments in the control module (Table 3). The lecturer observed that, when students were
engaged in EDS, they seemed to devote more time and effort as they tried to improve theirwork and produce the best end-products because they wanted to share them with their peers,
friends, and family. As regards to challenges that students faced, digital literacy was a main
issue for some non-traditional students. These learners should be offered systematic training
and support before using EDS. This will facilitate the use of this approach which has astatistically significant influence on students’ academic performance (Table 3), a finding
confirming previous research (Balaman, 2018).
Overall, the EDS intervention managed to increase cooperation and understanding among
students from various cultural and linguistic backgrounds and supported them in exchanging
ideas about their teaching practices as students shared their DS through the social media andinteracted with colleagues from around the world. The literature indicated that digital narratives
enhanced effective visualization of reflections and ideas which enabled sharing multicultural
views and thoughts (Fokides, 2016). Digital narration created a psychologically safe andsupportive foundation for intercultural and multilingual cooperation and learning (Fokides,2016) as learners in the present research study were also allowed to use short videos in theirown native language (with a translation in English). This was not possible with the traditional
writing assignments. Consequently, by adopting EDS, the lecturer was able to create a space
which promoted intercultural awareness and multilingual interaction in the context of anundergraduate module. The internationalization of HE can also be seen as an exchange among
various cultures. Given this stance, more initiatives that promote intercultural learning,plurilingualism and interaction among learners are vital.
EDS can engage university students through involvement in the creative process it necessitates(see Kocaman-Karoglu, 2016) and promote professional skills such as negotiation. Moreover,
EDS may involve learners in genuine learning and enhance their understanding of theories andmodule content (Table 5, Comment 2). Research showed that the use of EDS in HE is stilldeveloping but does provide new techniques for learners to organise and present their work inalternative ways as well as think critically (Chan et al., 2017). As an important new area ofstudy in HE, EDS may capture learner interest and develop valuable capabilities such as digitalskills, which are considered essential in the 21st century (Germaine et al., 2016). Gregori-Signs(2014) claimed that EDS enabled learners to “evaluate the reality that surrounds them andproduce their own interpretation of it” (p. 241). The current study, confirming previous
research (Smeda et al., 2014), reported that the ability of EDS to personalize student learning
experiences helped support students’ diverse cultural and linguistic backgrounds, increased
learner confidence, and developed writing, social and psychological skills (Tables 3, 4 and 5).

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Conclusion
Various studies have revealed that adopting EDS connects the high-tech world outside theuniversity and the ordinary typically lower-tech within the university setting, but also engages
learners in developing their skills through the creation of personal stories (Smeda et al., 2014).
This study advances the understanding of student writing, digital literacy, and motivational
enhancement through EDS tasks in multicultural and multilingual HE classrooms. Findings
will assist education faculty to use technologically suffused pedagogies to meet module aims
successfully as EDS can improve student writing performance and attitudes towards learning.
Additionally, having a real audience to view the stories increases learner motivation and helps
them improve their work by editing it more carefully. Lastly, the collected data revealed thatthe previous concerns of some of the students, which were linked to the use of technology andthe lack of time, gradually turned into positive feelings, including pride and enthusiasm about
their final stories (Table 5, Comment 5).
However, educators must still be cautious when implementing this exciting yet relatively newtechnique as students face considerable challenges when they have limited access to theInternet or technology. Moreover, some especially non-traditional students lack the relevant
knowledge and experience in using technological tools in education. Consequently,
practitioners should provide training and continuous support to their students when using EDS
to avoid inequity. In addition, educators must have a detailed plan, which can guide theirstudents. This should help students understand the reasons why they are asked to engage in
EDS and the benefits to academic and professional development. Lecturers should alsoemphasize the product as well as the process of learning as learners develop their academic
skills while preparing their DS. They also need to focus on assessing student writing skills
rather than the elements that learners use to make their stories more attractive. There should
also be clear assessment criteria that match curricular goals. Otherwise, the implementation
may not be successful and may be unfair for some students. Moreover, HEIs should provide
professional development courses to academic staff regarding these new technological tools
and encourage lecturers to use them in various modules. Although the current research was asmall-scale study limited in time and involved only 50 students at one university, this
implementation may be valuable as a genuine learning experience for undergraduate learners.
Therefore, additional research on the use of EDS in multicultural and multilingual HEI settings
is necessary. Future research should explore the use of EDS in other academic programs, forexample, at the doctoral level, or take into consideration other types of course delivery such as
blended learning that have become increasingly popular during the COVID-19 crisis.
Conducting interview based qualitative research to explore learners’ retention of various
learning theories and content would also be helpful to reveal the impact of EDS on academic
performance. In addition, quantitative longitudinal analysis on techniques to measure retention
and development of other skills, such as oral presentation skills, would be valuable. Further
research should examine the influence of this learning method on practicing teachers’ schoolbased activity regarding technology integration and the extent to which teachers use EDS in
their own classrooms after being exposed to it as students.
Being a reflective educator requires higher order thinking skills. Problem-solving and decisionmaking are seen as an integral part of being reflective with respect to student knowledge, socialand cultural circumstances, psychological processes, attitudes towards learning and teaching,and self-awareness about oneself as an educator (Schunk, 2012). Taking this into consideration,
this study aspired to introduce student-teachers to the significance of reflective practice through125

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EDS with the goal of enhancing writing skills and motivation. Based on the findings of thestudy, EDS is a reliable method to develop undergraduate student digital literacy and enhancetheir motivation to learn. Finally, the present study provides useful guidelines for educators in
HE to integrate EDS activities into their modules and courses.

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Corresponding author: Eleni Meletiadou
Email: elenim@outlook.com

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Reviewers – Volume 10 – Issue 2
The editorial team would like to thank the following reviewers for their contributions to thepeer review of this issue. Their dedication and assistance are greatly appreciated.
Senior Reviewers
Dr Shiao-wei Chu, National Chin-Yi University of Technology, Taiwan
Dr Melinda Cowart, Texas Woman’s University (TWU), USA
Dr Mikel Garant, Beijing Institute of Technology, China
Dr Elena Mishieva, Moscow State University, Russia
Dr Leonardo Munalim, The Philippine Women's University-School of Arts & Sciences andSchool of Education, The Philippines
Dr Murielle El Hajj Nahas, Lusail University, Qatar
Dr Shwadhin Sharma, California State University Monterey Bay, USA
Dr Michelle Yeo, Coventry University and University of Newcastle, Australia
Dr Serpil Meri-Yilan, Ibrahim Cecen University, Turkey
Reviewers
Dr Philip Ajewole, Federal University Oye-Ekiti, Nigeria
Dr Hee Seon Jang, Pyeongtaek University, Korea
Dr Michelangelo Magasic, Kochi University of Technology, JapanDr Debbie Meharg, Edinburgh Napier University, United Kingdom
Dr Nancy Nisbett, California State University, Fresno, USA
Dr Menyani Nourreddine, Tofail University Faculty of Arts and Humanities, Morocco
Dr Queen Ogbomo, Tennessee Technological University, USA
Dr Urarat Parnrod, Songkhla Rajabhat University, Thailand
Dr Metee Pigultong, Department of Educational Technology & Communications,Rajamangala University of Technology Thanyaburi, Thailand
Dr Elena Raevskikh, Department of Culture and Tourism, United Arab Emirates
Dr Stephanie Wendt, Tennessee Tech, USA

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ISSN: 2187-0594