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[Podcast] Decision-Making Assisted Model to Improve Course Evaluations at University of Economics Ho Chi Minh City

27 December, 2024

Keywords: Assisted decision-making, TOPSIS -AHP and Kansei, Course evaluation.

Course evaluations at universities play an important role in ensuring the quality of education. This process involves understanding the expectations and goals of students, the problems they are facing and giving appropriate advice. Course evaluations based on conventional methods are not applicable to criteria of varied importance. This research by University of Economics Ho Chi Minh City (UEH) will present a new approach using the TOPSIS – AHP and Kansei machine learning models to improve the effectiveness of course evaluation to contribute to helping students choose favorable courses, helping managers make timely decisions with various goals.

In the new era of development and integration, with breakthroughs in science and technology, the fourth industrial revolution is formed on the basis of the knowledge economy and the trend of proactive integration, strongly affecting all fields. Education and training are considered key to developing human potential and the strongest lever in providing human resources and talents for the development of science and technology. On the other hand, the development of science and technology affects the entire structure and quality of the education system. Therefore, improving the quality of education and training is the top requirement and concern in this field.

Evaluating the quality of university curricula is an indispensable step in developing and improving the quality of education and training. The implementation of pre-, during- and post-course evaluations in recent years has had a positive impact on the quality of the general education system. Evaluating the quality of courses is the first step in comprehensive quality assessment, helping to recognize the advantages and the limitations in the training undertaken by institutions, thereby promoting and adjusting the process to fit the target learners and creating a premise to improve the quality of the following courses. In this article, the UEH author proposed the TOPSIS – AHP – Kansei machine learning model to improve the assessment of course quality to provide students in choosing subjects for a new semester. The TOPSIS – AHP model aims to evaluate the course quantified by both qualitative and quantitative factors combined with the suggested Kansei model applied to quantify the level of student evaluation for each faculty at universities.

Figure 1. General working framework

The proposed model as shown in Figure 1 includes 9 main modules as follows:

  • Input data: Including course information and course reviews by experts and students
  • Expert Data Receiver: Receiving data in the form of input values ​​– corresponding to each criterion in the database as training data.
  • TOPSIS Calculation: The TOPSIS algorithm calculates the order of ranking and evaluation of expert and student inputs.
  • Importance Conversion: Calculating and converting data from “professional data receiving mechanism” to corresponding importance levels
  • AHP Calculation: Using AHP Algorithm to evaluate courses
  • Student data recipient evaluation: Users who will participate in the survey.
  • Kansei Model Calculation: Calculating the Kansei evaluation results according to each criterion.
  • TOPSIS Calculation: Building a decision matrix and calculate based on expert assessments.
  • Output data: The most suitable course or the most suitable course evaluation result.

The proposed model has been tested to evaluate the quality of 03 courses at University of Economics Ho Chi Minh City. In course evaluation, experts/students select courses and evaluate the them according to the evaluation criteria of the modules. The proposed system will analyze, calculate and give a ranking (or evaluation) of the collected data to find the expert with the most positive rating. To evaluate the multidimensional level of positivity or negativity, the expert with the best set of evaluation indices can be selected as follows:

  • Expert with the most positive rating (highest dataset)
  • Experts with an average rating (average of the rating)

The decision maker can choose the positive or neutral evaluation results to continue evaluating the quality of the course. The summary of student evaluation results for the recently completed course is listed below as shown in the following figures.

Figure 2. Expert ranking based on the proposed model

Results of the course evaluation according to 5 levels: Very good, Good, Fairly good, Average, and Poor as shown below.

Figure 3. Course evaluation results

This study proposes the TOPSIS-AHP-Kansei model to improve the evaluation of courses at the University of Economics Ho Chi Minh City, combining knowledge from experts/lecturers/students to develop an evaluation process. The experimental results showed that the proposed course quality evaluation model has demonstrated a multidimensional impact on the positive performance of experts/students in the evaluation model. This evaluation method combined with traditional evaluation will provide accurate evaluations of the course and suggestions for managers in improving the quality of courses at the University in particular and the training unit in general. Expanding this research model, the author continues to expand the TOPSIS-AHP-Kansei model with more intuitive evaluation results. A knowledge base will be built to store specialized knowledge to reduce service costs and improve the quality of consulting for quality courses.

The full-text article on Building a decision-making assisted model using TOPSIS – AHP combined with the Newhouse ICT index to select smart courses at University of Economics Ho Chi Minh City can be accessed HERE.

Author: Dr. Truong Viet Phuong – University of Economics Ho Chi Minh City.

This article is part of the series spreading research and applied knowledge from UEH with the message “Research Contribution For All”. UEH cordially invites readers to read the next UEH Research Insights newsletter.

News and photos: Author, UEH Department of Communications and Partnership