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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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MDPI AG
01.06.2025
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| ISSN: | 2673-4591 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Long-Sheng Chen Tzu-Chuen Lu Thao-Trang Huynh-Cam Hai-Duy Le Vo Phan Thu Ngan |
| Author_xml | – sequence: 1 fullname: Hai-Duy Le organization: Thuan Hoa High School, Hue City 952410, Vietnam – sequence: 2 fullname: Thao-Trang Huynh-Cam organization: Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan – sequence: 3 fullname: Long-Sheng Chen organization: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106344, Taiwan – sequence: 4 fullname: Vo Phan Thu Ngan organization: Foreign Languages Faculty, Dong Thap University, Cao Lanh City 81118, Vietnam – sequence: 5 fullname: Tzu-Chuen Lu organization: Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan |
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| SubjectTerms | artificial intelligence algorithms Chinese (Hoa) students high-school minority students Khmer students learning performance learning performance predictions |
| Title | Predicting the Learning Performance of Minority Students in a Vietnamese High School Using Artificial Intelligence Algorithms |
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