Journal of Advances in Artificial Life Robotics
Vol. 4(4); March (2024), pp. 200–203
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

SUGISAKAMASANORIInternational Journal

Advances in Artificial Life Robotics
ICAROB series
Editor-in-Chief
Masanori Sugisaka
Co-Editor-in-Chief
Takao Ito

ISSN 2435-8061
Publisher Masanori Sugisaka(Global Register of Publishes)
regstrated in Jan, 25, 1996
Handadai Higashi 2-8-4, Oita, 870-1108, Japan

Research Article

Analysis of Educational Quality in University Courses Utilizing
Process Assessment
Yuhao Zhang1, Jiashuang Sun2, Xiaoran Ma3

College of Artificial Intelligence, Tianjin University of Science and Technology, 300222，China
College of Art and Design, Tianjin University of Science and Technology, 300222，China
3
College of Economics and Management, Tianjin University of Science and Technology, 300222，China
12

ARTICLE INFO
Article History
Received 11 November 2023
Accepted 11 July 2024
Keywords
University process assessment
Learning attitude
Machine learning
TOPSIS
Evaluation model

ABSTRACTThe progressive integration of formative assessment into university education has led to its
widespread adoption for evaluating students' daily learning behaviors and conditions. This paperleverages data on classroom learning experiences from a specific university, utilizing machinelearning, K-means clustering, the TOPSIS evaluation model, and the entropy weighting method.
The study aims to explore the correlation between formative assessment and the quality ofuniversity student learning, ultimately developing an evaluation model. This model identifies key
factors influencing student learning attitudes, supporting the use of formative assessment in highereducation.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction
2. Data Analysis

With the ongoing reform of educational systems, processassessment has gained significant traction among
universities. Unlike outcome assessments, processassessments provide a more comprehensive evaluation of
students' learning attitudes and conditions [1].
In process assessments, students' learning attitudes areinfluenced by both external environments and internalmotivations [2]. This study examines the factors impacting
students' learning attitudes, constructs a mathematicalmodel for evaluating learning styles, and develops anevaluation model to reflect students' enthusiasm forlearning.
This paper is structured as follows: Section 2 analyzes
the data and factors influencing students' learning attitudes.
Section 3 introduces the evaluation model construction.
Section 4 discusses the model's results. Section 5summarizes the paper's main findings.

The data collected include student numbers, colleges,classes, course codes, class codes, teacher codes, testnames, test times, test full scores, student scores, andnormalized scores. Students select different courses within
the core basic curriculum, with some choosing multiple
courses and others selecting only one. Not all students
participate in every test, and different teachers may not
post pre-class tests consistently.
To identify the factors most impacting students' learning
attitudes, various control variables are explored. The
following sections examine the influence of college factors,
the number of courses selected by students, student
attendance rates, and the roles of major, class, course,
teacher, and classroom on learning attitudes.

Corresponding author’s E-mail: a15839176622@163.com URL: www.tust.edu.cn

2002.1. College factor

each course. This allowed us to assess the correlation
between attendance and student performance. Fig. 3 andFig. 4 illustrate the relationships between attendance rates
and the representative scores.

The average scores of students in different colleges arecalculated and summarized, yielding the average andvariance of scores for each college. The data are visualizedin Fig. 1.

Fig.3 Scatter plot of attendance
Fig.1 College factor visualizationCount the number of people with scores greater than 80
in each attendance range and draw a bar chart, which isshown in Fig. 4.

The scores and their stability vary across colleges,
indicating differences in learning atmospheres and,
consequently, student attitudes. Thus, college factors
influence students' learning attitudes.
2.2. Number of courses taken
We calculate the average of each student's normalized
scores across tests as their representative score. The
number of courses selected by each student is categorized,
and their representative scores are visualized in Fig. 2.

Fig.4 attendance and high score statistics
As can be seen from Fig. 3 and Fig. 4, Higher attendance
rates were found to correlate strongly with better scores.
Students who attended more classes tended to achieve
higher grades, indicating that regular attendance positively
impacts learning attitudes and overall academicperformance. This correlation highlights the importance ofconsistent participation in classes for academic success.
2.4. Major, class, curriculum, teacher factor

Fig.2 Number of Selected Courses

We calculated the average grades and their variances foreach major, class, curriculum, and teacher. Our analysis
showed the following:
Major: While the major a student belongs to does
influence learning attitudes, the effect is relatively minor
compared to other factors.
Class: The class a student is in significantly impacts
their learning attitude. Different classes exhibited varying
levels of enthusiasm and performance, indicating that theAs shown from Fig. 2, students' scores tend to stabilize
and improve with the number of courses selected,
suggesting that more courses correlate with higher
enthusiasm and better attitudes.
2.3. Student attendance
To determine the attendance rates, we calculated the ratioof attended tests to the total number of tests available for201

class environment plays a crucial role in shaping student
attitudes.
Curriculum: The specific courses students take also
affect their overall performance and attitude towards
learning. Certain courses are associated with higher
engagement and better outcomes.
Teacher: The influence of the teacher on student
performance and stability is noteworthy. Different
teaching styles and methods can lead to varying levels ofstudent engagement and success.
This comprehensive analysis indicates that classenvironment and teaching quality are substantial
determinants of students' learning attitudes andperformance.

minimum value for each column. The distance of the 𝑖𝑖-th
evaluation object to the maximum value is given by:+𝐷𝐷𝑖𝑖+ = �∑𝑚𝑚
𝑗𝑗=1 ω𝑗𝑗 �𝑍𝑍𝑗𝑗 − 𝑧𝑧𝑖𝑖𝑖𝑖 �2(1)2(2)

Similarly, the distance of the 𝑖𝑖-th evaluation object to theminimum value is defined as:−𝐷𝐷𝑖𝑖− = �∑𝑚𝑚
𝑗𝑗=1 ω𝑗𝑗 �𝑍𝑍𝑗𝑗 − 𝑧𝑧𝑖𝑖𝑖𝑖 �Next, the unnormalized score for the 𝑖𝑖-th evaluation object
is calculated using the formula:
𝐷𝐷 −𝑖𝑖
𝑆𝑆𝑖𝑖 = 𝐷𝐷++𝐷𝐷
−𝑖𝑖𝑖𝑖

(3)

In this equation, 0 ≤ 𝑆𝑆𝑖𝑖 ≤ 1, and a larger 𝑆𝑆𝑖𝑖 corresponds
to a smaller 𝐷𝐷𝑖𝑖+ , indicating that the evaluation object iscloser to the maximum value. By applying the indicatorweights to the sample distance calculations, we determine
the final score for each sample [3].

3. Construction of Evaluation Model
We aggregated data for all students within each college
and class to calculate the average test scores, test
participation rates, and the number of courses taken by
students in each group. This process yielded a dataset thatincludes the college (or class), average test score, average
test participation rate, and number of courses taken. Given
the limited number of indicators, we employed theTOPSIS method for evaluation [3].

4. Introduction of Result
Using the constructed model, MATLAB calculations yield
the final results. The evaluation hierarchy is shown in Fig.
6.

3.1. Data standardization
As attendance, GPA, and the average number of courses
taken are all positive indicators, the dataset—comprisingcollege (or class), average test score, average test
participation rate, and number of courses taken—wasstandardized accordingly.

Fig.6 evaluation hierarchy diagram
4.1. Personal evaluation model

3.2. Calculate index weight

Students are clustered using K-Means into better andworse groups, which are then evaluated by TOPSIS basedon attendance rates, courses selected, and individual
performance [2].

To ensure the index weights are both objective andscientifically valid, we used the entropy weight method
combined with the analytic hierarchy process. The
resulting weights are displayed in Fig. 5.

5. Conclusion
This study constructs a process evaluation model based on
students' routine performances, identifies factors
influencing learning attitudes, and highlights wellperforming classes, colleges, and students. The findings
will assist universities in enhancing process assessment
practices.

Fig.5 index weight
3.3. Calculating the score
To calculate the scores, we first define 𝑍𝑍 + as the maximum
value for each column in the vector matrix and 𝑍𝑍 − as the202

References
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2.

3.

Li Q, Li Z, Han J, et al., Quality Assurance for Performing
Arts Education: A Multi-Dimensional Analysis Approach,Applied Sciences, 2022, 12(10): 4813.
Zhang Rui, Xu Peng, Fang Mingfeng, a process-based anddiversified curriculum assessment method for local
comprehensive universities, Journal of Chongqing
University of Technology (Social Sciences), 2014, 28(04):129-133.
Yu Xiaofen, Fu Dai, A review of multi-index
comprehensive evaluation methods, Statistics and Decision,
2004(11): 119-121.

Authors Introduction
Mr. Yuhao Zhang

Ms. Jiashuang Sun

Ms. Xiaoran Ma

He is an undergraduate majoring inartificial intelligence at Tianjin
UniversityofScienceandTechnology. He learns about
electronic information and artificial
intelligence, and currently focuses on
digital image processing, deeplearning and pose estimation.

She is an undergraduate majoring invisual communication design in
Tianjin University of Science andTechnology. She studies all kinds ofgraphic design and artificial
intelligence AIGC, and currentlyfocuses on brand image design andartificial intelligence AIGC.

She is an undergraduate majoring in
Public Administration at Tianjin
UniversityofScienceandTechnology.
Shestudies
administration and public service
management, and currently focuses
on research in administrative
management and public policy.

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