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.
Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019). Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques. In Technology, Knowledge and Learning (Vol. 24, Issue 4). Springer Netherlands. https://doi.org/10.1007/s10758-019-09408-7
Al-Barrak, M. A., & Al-Razgan, M. (2016). Predicting Students Final GPA Using Decision Trees: A Case Study. International Journal of Information and Education Technology, 6(7), 528–533. https://doi.org/10.7763/ijiet.2016.v6.745
Ali, G., Jaaffar, A. R., & Ali, J. (2021). STEM Education in Malaysia: Fulfilling SMEs’ Expectation. In Modeling Economic Growth in Contemporary Malaysia (pp. 43–57). Emerald Group Publishing Ltd. https://doi.org/10.1108/978-1-80043-806-420211005
Asiah, M., Nik Zulkarnaen, K., Safaai, D., Nik Nurul Hafzan, M. Y., Mohd Saberi, M., & Siti Syuhaida, S. (2019). A Review on Predictive Modeling Technique for Student Academic Performance Monitoring. MATEC Web of Conferences, 255, 03004. https://doi.org/10.1051/matecconf/201925503004
Asif, R., Merceron, A., & Pathan, M. K. (2014). Predicting Student Academic Performance at Degree Level: A Case Study. International Journal of Intelligent Systems and Applications, 7(1), 49–61. https://doi.org/10.5815/ijisa.2015.01.05
Barbosa Manhães, L. M., da Cruz, S. M. S., & Zimbrão, G. (2015). Towards automatic prediction of student performance in STEM undergraduate degree programs. 247–253. https://doi.org/10.1145/2695664.2695918
Devasia, T., Vinushree, T. P., & Hegde, V. (2016). Prediction of students performance using Educational Data Mining. Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, 91–95. https://doi.org/10.1109/SAPIENCE.2016.7684167
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Data Mining and Knowledge Discovery in Databases. Communications of the ACM, 39(11), 24–26. https://doi.org/10.1145/240455.240463
Huang, R., Spector, J. M., & Yang, J. (2019). Educational Technology a Primer for the 21st Century. In Journal of Educational Television (Vol. 1, Issue 2). https://doi.org/10.1080/1358165750010212
Livieris, I. E., Drakopoulou, K., Tampakas, V. T., Mikropoulos, T. A., & Pintelas, P. (2019). Predicting Secondary School Students’ Performance Utilizing a Semi-supervised Learning Approach. Journal of Educational Computing Research, 57(2), 448–470. https://doi.org/10.1177/0735633117752614
Lowis, M., & Castley, A. (2008). Factors affecting student progression and achievement: Prediction and intervention. A two-year study. Innovations in Education and Teaching International, 45(4), 333–343. https://doi.org/10.1080/14703290802377232
Mokhtar, S., Alshboul, J. A. Q., & Shahin, G. O. A. (2019). Towards Data-driven Education with Learning Analytics for Educator 4.0. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1339/1/012079
Mueen A, Zafar B, Manzoor U. Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques. Int J Mod Educ Comput Sci. 2016;8(11):36–42.
Perner, P. (2015). Machine Learning and Data Mining in Pattern Recognition 11th International Conference, MLDM 2015 Hamburg, Germany, July 20-21, 2015 Proceedings. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9166, 403–414. https://doi.org/10.1007/978-3-319-21024-7
Ramaswami, G., Susnjak, T., Mathrani, A., Lim, J., & Garcia, P. (2019). Using educational data mining techniques to increase the prediction accuracy of student academic performance. Information and Learning Science, 120(7–8), 451–467. https://doi.org/10.1108/ILS-03-2019-0017
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. https://doi.org/10.1016/j.eswa.2006.04.005
Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532
Roslan, M. H. bin, & Chen, C. J. (2022). Educational Data Mining for Student Performance Prediction: A Systematic Literature Review (2015-2021). International Journal of Emerging Technologies in Learning, 17(5), 147–179. https://doi.org/10.3991/ijet.v17i05.27685
Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Sumathi, S., & Sivanandam, S. N. (2006). Introduction to Data Mining and its Applications. In Studies in Computational Intelligence (Vol. 29). https://doi.org/10.1007/978-3-540-34351-6
UNESCO. (2017). Cracking the code: Girls' and women's education in science, technology, engineering, and mathematics (STEM).
https://unesdoc.unesco.org/ark:/48223/pf0000245656
Witten, I.H., Frank, E. and Hall, M.A. (2011) Data Mining: Practical Machine Learning Tools and Techniques. 3rd Edition, Morgan Kaufmann Publishers, Burlington.
Zulkifli, F., Mohamed, Z., & Azmee, N. A. (2019). Systematic research on predictive models on students’ academic performance in higher education. International Journal of Recent Technology and Engineering, 8(2 Special Issue 3), 357–363. https://doi.org/10.35940/ijrte.B1061.0782S319
Copyright: © 2023 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
