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 poin...

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Veröffentlicht in:Engineering proceedings Jg. 98; H. 1; S. 22
Hauptverfasser: Hai-Duy Le, Thao-Trang Huynh-Cam, Long-Sheng Chen, Vo Phan Thu Ngan, Tzu-Chuen Lu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: MDPI AG 01.06.2025
Schlagworte:
artificial intelligence algorithms
Chinese (Hoa) students
high-school minority students
Khmer students
learning performance
learning performance predictions
ISSN:2673-4591
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Zusammenfassung: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.
ISSN:2673-4591
DOI:10.3390/engproc2025098022