[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122488-en":3,"doc-seo-122488-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122488,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting Students’ Performance in Mathematics Subjects at Kolej MARA Banting using Machine Learning Methods","Predicting students’ performance is crucial for personalized educational success, yet no standard method accounts for external factors to forecast mathematics outcomes at Kolej MARA Banting (KMB). This research applies machine learning through a pipeline of data collection, attribute selection, preprocessing, model training, and evaluation using 703 student records covering demographics, academic history, and mathematics performance. Models including SVM, decision tree, k-NN, Naïve Bayes, Random Forest, AdaBoost, and stacking are compared. Results show stacking performs best with moderate accuracy (71.43% accuracy; F1-score 69.80%), while bias from imbalanced IB grade distribution and underfitting limit improvement; recommendations include adding more features and using data from other colleges with balanced grade distributions.","Predicting Students’ Performance in Mathematics Subjects at Kolej MARA Banting using Machine Learning Methods  \nRamalan Prestasi Pelajar dalam Mata Pelajaran Matematik di Kolej MARA Banting MenggunakanKaedah PembelajaranMesin  \nAhmad Akif Ibrahim1, Nor Azuana Ramli2* & Sahimel Azwal Sulaiman2  \n1Mathematics Department, Kolej MARA Banting, Jalan Labohan Dagang, Bukit Changgang, 42700  \nBanting, Selangor, Malaysia.  \n2Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300 Kuantan, Pahang, Malaysia.  \n*[Corresponding author: ](Corresponding author: azuana@umpsa.edu.my)[azuana@umpsa.edu.my](Corresponding author: azuana@umpsa.edu.my)  \nDiterima: 28 Ogos 2024; Disemak semula: 25 Januari 2024 Diterima: 06 April 2025; Diterbitkan: 28 April 2025  \nTo cite this article (APA): Ibrahim, A. A. ., Ramli, N. A. ., & Sulaiman, S. A. . (2025) . Predicting Students’Performance in Mathematics Subjects at Kolej MARA Banting using Machine Learning Methods. Jurnal Pendidikan Sains Dan Matematik Malaysia, 15(1), 19-31. [https://doi.org/10.37134/jpsmm.vol15.1.2.2025](https://doi.org/10.37134/jpsmm.vol15.1.2.2025)  \nTo link to this article: [https://doi.org/10.37134/jpsmm.vol15.1.2.2025](https://doi.org/10.37134/jpsmm.vol15.1.2.2025)  \nABSTRACT  \nPredicting students’ performance is crucial for personalised and educational success for individuals. However, no standard procedure or method considers external factors to predict students’ performance in mathematics at Kolej MARA Banting (KMB). This research aims to address this problem by exploring the potential of machine learning methods for predicting students’ performance in mathematics at KMB. The study follows a machine learning process: data collection, attribute selection, pre-processing, model training, and evaluation. A sample of 703 data points on students’ demographics, academic records, and mathematics performance were collected and preprocessed. Machine learning models such as support vector machine, decision tree, k-nearest neighbours, Naïve Bayes, Random Forest, AdaBoost, and stacking model were applied in this study. The accuracy and performance of these models were assessed to determine which model outperformed the others and its effectiveness in predicting students’ mathematics performance. The study findings demonstrate that the stacking model exhibited superior performance in accuracy (71.43%), precision (68.73%), recall (71.43%), and F1-score (69.80%) compared to the other models. Nevertheless, it is essential to note that the stacking model achieved moderate accuracy. This could be attributed to the inherent difficulties in constructing a precise predictive model for student performance, such as the models failing to sufficiently reflect the complexities within the dataset, resulting in underfitting. Additionally, the target attribute, International Baccalaureate (IB) grade, is imbalanced, with more high performers than low performers, causing the models to be biased towards the majority class and impacting overall accuracy. The performance of the models in this study could be improved by adding more features related to students’ performance, such as anxiety, depression, well-being, and others, to capture enough complexity in the data. It is also suggested that samples from other colleges with a balanced grade distribution be obtained compared to students at KMB.  \nKeywords: Machine Learning, Students ’ Performance, Mathematics Subjects, International Baccalaureate, Predictive Modelling  \nABSTRAK  \nMeramal prestasi pelajar adalah penting bagi kejayaan peribadi dan pendidikan seseorang. Walaubagaimanapun, sehingga kini tiada prosedur standard dan kaedah yang mempertimbangkan faktor luaran untuk meramalkan prestasi pelajar dalam subjek matematik diKolejMARA Banting (KMB). Kajian ini bertujuan untuk membangunkan model ramalan dengan menggunakan kaedahpembelajaran mesin bagi meramal prestasi para pelajar dalam subjek mate","cbCaiqGd0yXI3AUU","https://ap.wps.com/l/cbCaiqGd0yXI3AUU","pdf",551588,1,13,"English","en",105,"# Abstract\n## Study aim and approach\n## Data and models\n## Evaluation results\n## Limitations and improvement suggestions","[{\"question\":\"What problem does the research address at Kolej MARA Banting (KMB)?\",\"answer\":\"It addresses the lack of a standard procedure that considers external factors to predict students’ performance in mathematics at KMB.\"},{\"question\":\"Which machine learning models were used to predict mathematics performance?\",\"answer\":\"The study applied SVM, decision tree, k-NN, Naïve Bayes, Random Forest, AdaBoost, and stacking (stacked ensemble) models.\"},{\"question\":\"Why is the stacking model’s predictive performance only moderate?\",\"answer\":\"The model may underfit because it does not capture the dataset’s complexity sufficiently, and the IB target grade is imbalanced, causing bias toward the majority class.\"}]","Predicting Students’ Performance in Mathematics Subjects at Kolej MARA Banting using Machine Learning Methods | 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problem does the research address at Kolej MARA Banting (KMB)?","Question",{"text":75,"@type":76},"It addresses the lack of a standard procedure that considers external factors to predict students’ performance in mathematics at KMB.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used to predict mathematics performance?",{"text":80,"@type":76},"The study applied SVM, decision tree, k-NN, Naïve Bayes, Random Forest, AdaBoost, and stacking (stacked ensemble) models.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is the stacking model’s predictive performance only moderate?",{"text":84,"@type":76},"The model may underfit because it does not capture the dataset’s complexity sufficiently, and the IB target grade is imbalanced, causing bias toward the majority 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