Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education
Educational Data Mining (EDM) applies data mining in education, aiding schools to enhance student learning programs by analyzing data and success factors. In the era of big data, schools must adopt data-driven approaches. However, predicting success among diverse secondary students in Malaysia remai...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Human Resource Management Academic Research Society
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/108442/ http://psasir.upm.edu.my/id/eprint/108442/1/108442.pdf |
| _version_ | 1848867713592590336 |
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| author | Termedi, Mohammad Izzuan Ma’rof, Aini Marina Ab. Jalil, Habibah Ishak, Iskandar |
| author_facet | Termedi, Mohammad Izzuan Ma’rof, Aini Marina Ab. Jalil, Habibah Ishak, Iskandar |
| author_sort | Termedi, Mohammad Izzuan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Educational Data Mining (EDM) applies data mining in education, aiding schools to enhance student learning programs by analyzing data and success factors. In the era of big data, schools must adopt data-driven approaches. However, predicting success among diverse secondary students in Malaysia remains uncertain due to dataset size and heterogeneity. This study aims to identify key predictor variables for STEM student performance and present a systematic method for analysis, benefiting academics, schools, and the education ministry. The article explores data mining via knowledge discovery (KDD) and employs classifiers like Random Forest, PART, J48, and Naive Bayes on a dataset of Malaysian upper-secondary Science students. Utilizing WEKA for analysis, the research utilizes 21 features from the Education Repository and SAPS. Notably, J48 outperforms other classifiers. The study aids educational enhancement, enabling early intervention and improved academic achievement. |
| first_indexed | 2025-11-15T14:40:52Z |
| format | Article |
| id | upm-108442 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:40:52Z |
| publishDate | 2023 |
| publisher | Human Resource Management Academic Research Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1084422025-08-06T07:16:39Z http://psasir.upm.edu.my/id/eprint/108442/ Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education Termedi, Mohammad Izzuan Ma’rof, Aini Marina Ab. Jalil, Habibah Ishak, Iskandar Educational Data Mining (EDM) applies data mining in education, aiding schools to enhance student learning programs by analyzing data and success factors. In the era of big data, schools must adopt data-driven approaches. However, predicting success among diverse secondary students in Malaysia remains uncertain due to dataset size and heterogeneity. This study aims to identify key predictor variables for STEM student performance and present a systematic method for analysis, benefiting academics, schools, and the education ministry. The article explores data mining via knowledge discovery (KDD) and employs classifiers like Random Forest, PART, J48, and Naive Bayes on a dataset of Malaysian upper-secondary Science students. Utilizing WEKA for analysis, the research utilizes 21 features from the Education Repository and SAPS. Notably, J48 outperforms other classifiers. The study aids educational enhancement, enabling early intervention and improved academic achievement. Human Resource Management Academic Research Society 2023 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/108442/1/108442.pdf Termedi, Mohammad Izzuan and Ma’rof, Aini Marina and Ab. Jalil, Habibah and Ishak, Iskandar (2023) Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education. International Journal of Academic Research in Progressive Education and Development, 12 (4). 225 - 242. ISSN 2226-6348 https://ijarped.com/index.php/journal/article/view/793/766 10.6007/IJARPED/v12-i4/19577 |
| spellingShingle | Termedi, Mohammad Izzuan Ma’rof, Aini Marina Ab. Jalil, Habibah Ishak, Iskandar Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education |
| title | Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education |
| title_full | Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education |
| title_fullStr | Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education |
| title_full_unstemmed | Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education |
| title_short | Utilizing educational data mining for enhanced student performance analysis in Malaysian STEM education |
| title_sort | utilizing educational data mining for enhanced student performance analysis in malaysian stem education |
| url | http://psasir.upm.edu.my/id/eprint/108442/ http://psasir.upm.edu.my/id/eprint/108442/ http://psasir.upm.edu.my/id/eprint/108442/ http://psasir.upm.edu.my/id/eprint/108442/1/108442.pdf |