ISSN: 2226-6348
Open access
The student's performance has become the focus in higher education institutions. The ability to predict students' performance is beneficial to improve their achievement and the learning process. However, producing a prediction model for academic performance becomes challenging when an educational dataset contains various data. Many researchers have widely explored this kind of research, but many features should be investigated to affect students' achievement. Finding the potential factors influencing students' performance helps enhance students' quality. These factors will assist an institution plan a strategy for improving students' performance. This research proposes a classifier model to predict students' academic performance and define the factors influencing the performance by considering 14 attributes from demographics, learning styles, and educational background. The model development employs seven machine learning algorithms, and the best model will be selected. The factors that influence academic performance will be revealed from that model. The dataset was collected by conducting a survey at UiTM Seremban involving 233 students from Science and Technology and Social Science Streams. The Random Forest Tree produced an accurate result with the simple rules to be interpreted. The model also showed four attributes: qualification before tertiary education, SPM result, Seniority and gender positively impacting academic performance. Some factors that did not influence their performance were their parents' academic background and hometown.
Ahmed, R. M., Ali, A. A., Omran, N., & Omran, N. F. (2018). Predicting and Analysis of Students' Academic Performance using Data Mining Techniques. In International Journal of Computer Applications (Vol. 182, Issue 32).
https://www.researchgate.net/publication/347520956
Alyahyan, E., & Dü?tegör, D. (2020). Predicting Academic Success In Higher Education: Literature Review And Best Practices. In International Journal of Educational Technology in Higher Education (Vol. 17, Issue 1). Springer. https://doi.org/10.1186/s41239-020-0177-7
Amirah, W., Azlan, W., Liew, S.-H., Choo, Y.-H., Zakaria, H., & Low, Y. F. (2016). Wavelet Feature Extraction And J48 Decision Tree Classification Of Auditory Late Response (Alr) Elicited By Transcranial Magnetic Stimulation. 11(10).
http://users.aber.ac.uk/rkj/book/wekafull.jar.
Bhatia, S., & Malhotra, J. (2021). Naïve Bayes Classifier For Predicting The Novel Coronavirus. Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 880–883. https://doi.org/10.1109/ICICV50876.2021.9388410
Ghazal, A., & Allah, F. (2020). Using Machine Learning To Support Students' Academic Decisions. Journal Of Theoretical and Applied Information Technology, 98(18). www.jatit.org
Gopagoni, D. R., & Lakshmi, P. v. (2020). Automated Machine Learning Tool: The First Stop For Data Science And Statistical Model Building. International Journal of Advanced Computer Science and Applications, 2, 410–418.
https://doi.org/10.14569/ijacsa.2020.0110253
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting Student Performance In Higher Educational Institutions Using Video Learning Analytics And Data Mining Techniques. Applied Sciences (Switzerland), 10(11). https://doi.org/10.3390/app10113894
Husam, I. S., Abuhamad, A. A. B., Zainudin, S., Sahani, M., & Ali, Z. M. (2017). Feature Selection Algorithms For Malaysian Dengue Outbreak Detection Model. Sains Malaysiana, 46(2), 255–265. https://doi.org/10.17576/jsm-2017-4602-10
Ilçin, N., Tomruk, M., Ye?ilyaprak, S. S., Karadibak, D., & Savcl, S. (2018). The Relationship Between Learning Styles And Academic Performance In Turkih Physiotherapy Students ,Specialist Studies In Education. BMC Medical Education, 18(1).
https://doi.org/10.1186/s12909-018-1400-2
Melo, G. O. (2018). Enhancing Oral Skill Through Learning Styles VAK Enhancing Oral Skill Development Through Learning Styles VAK.
http://campus.educadium.com/newmediart/file.php/137/Thesis_Repository/recds/assets/T
Mengash, H. A. (2020). Using Data Mining Techniques To Predict Student Performance To Support Decision Making In University Admission Systems. IEEE Access, 8, 55462–55470. https://doi.org/10.1109/ACCESS.2020.2981905
Nabil, A., Seyam, M., & Abou-Elfetouh, A. (2021). Prediction of Students' Academic Performance Based on Courses' Grades Using Deep Neural Networks. IEEE Access, 9, 140731–140746. https://doi.org/10.1109/ACCESS.2021.3119596
Nuankaew, P., Nuankaew, W., Phanniphong, K., Imwut, S., & Bussaman, S. (2019). Students Model In Different Learning Styles Of Academic Achievement At The University Of Phayao, Thailand. International Journal of Emerging Technologies in Learning, 14(12), 133–157. https://doi.org/10.3991/ijet.v14i12.10352
Poudyal, S., Mohammadi-Aragh, M. J., & Ball, J. E. (2022). Prediction of Student Academic Performance Using a Hybrid 2D CNN Model. Electronics (Switzerland), 11(7). https://doi.org/10.3390/electronics11071005
Qian, W., Huang, J., Wang, Y., & Xie, Y. (2021). Label Distribution Feature Selection For Multi-Label Classification With Rough Set. International Journal of Approximate Reasoning, 128, 32–55. https://doi.org/10.1016/j.ijar.2020.10.002
Razak, R. A., Omar, M., & Ahmad, M. (2018). A Student Performance Prediction Model Using Data Mining Technique. In International Journal of Engineering & Technology (Vol. 7, Issue 2).
Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An Overview And Comparison Of Supervised Data Mining Techniques For Student Exam Performance Prediction. Computers and Education, 143. https://doi.org/10.1016/j.compedu.2019.103676
Ya?c?, M. (2022). Educational Data Mining: Prediction Of Students' Academic Performance Using Machine Learning Algorithms. Smart Learning Environments, 9(1). https://doi.org/10.1186/s40561-022-00192-z
In-Text Citation: (Yusof et al., 2022)
To Cite this Article: Yusof, R., Hashim, N., Rahman, N. A., Yunus, S. Y. M., & Fadzillah, N. A. A. (2022). Academic Performance Prediction Model Using Classification Algorithms: Exploring the Potential Factors. International Journal of Academic Research in Progressive Education and Development, 11(3), 706–724.
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