Vu Dinh Chien

Work place: Dak Lak College of Pedagogy, Dak Lak, 63000, Vietnam



Research Interests: Data Science, Machine Learning


Vu Dinh Chien is currently working at Dak Lak College of Pedagogy, Vietnam. He got his BSc. and MSc. in Computer Science from Quy Nhon University and Hanoi National University of Education, Vietnam, respectively. His research interest includes data science and machine learning.

Author Articles
Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression

By Dao Thi Thanh Loan Nguyen Duy Tho Nguyen Huu Nghia Vu Dinh Chien Tran Anh Tuan

DOI:, Pub. Date: 8 Feb. 2024

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

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