Predicting the Learning Performance of Minority Students in a Vietnamese High School Using Artificial Intelligence Algorithms

This study aims to predict and discover important factors for the learning performance of students belonging to two ethnic groups—Khmer and Chinese (Hoa) students—in Soc Trang with the use of random forest (RF) and Gaussian Naïve Bayes (GNB) classifiers based on students’ demographics and grade point average (GPA) scores. The study involved 174 Khmer and Chinese (Hoa) students in Grade 10 in a high school in Soc Trang Province, Vietnam. The results showed that, for Khmer students, GNB was better than RF, with an F1 score of 100%. Mathematics was the most important subject leading Khmer students to very good or poor performance. For Chinese (Hoa) students, both classifiers showed the same accuracy performance. Scores in Literature and English in Semester 1 impacted Chinese (Hoa) students’ performance. The results of this study provide a reference for formulating a policy to improve the learning performance of minority students to prevent dropouts.