USING MARKOV CHAIN TO MODEL THE LEARNING PROCESS OF STUDENTS FROM THE FACULTY OF EDUCATION AT THU DAU MOT UNIVERSITY

By Le Thi Thu
DOI: 10.37550/tdmu.EJS/2025.04.684

This study applies a first-order Markov chain to analyze and model the academic progression of 317 students from the Faculty of Education at Thu Dau Mot University, utilizing their semester Grade Point Averages (GPA) as the core data. Students' GPAs were methodologically classified into four distinct academic performance states: Weak (0–4.99), Average (5.0–6.99), Good (7.0–7.99), and Excellent (8.0–10.0). Transition matrices were constructed to capture the movements between these performance states across consecutive semesters. Descriptive analysis reveals a positive performance trend, specifically a frequent transition from the Average to the Good group, and a high level of stability observed within the Excellent group, particularly in the later stages of the program. A crucial Chi-square test for homogeneity revealed statistically significant differences, indicating that the learning process is non-homogeneous over time, reflecting fluctuations in student learning behavior. However, to fulfill the objective of forecasting the expected distribution of student performance in the subsequent semester, a weighted average transition matrix was computed, giving greater emphasis to the influence of more recent academic data. Forecasting results suggest that approximately 90% of students are expected to concentrate within the Good and Excellent categories, confirming a high standard of academic performance and providing valuable empirical evidence for targeted student support and curriculum management within the Faculty of Education.