ISSN: 2226-6348
Open access
The discipline of higher education is seeing rapid growth and is closely intertwined with the advancements in technology. Utilisation of machine learning (ML) to predict students' academic achievement has demonstrated promising results and has been advantageous for educational institutions. The challenges associated with making predictions reside in the ability to accurately identify potential attributes within multi-class projections, while also considering the varying quantities of distinct attribute categories. Therefore, this study has examined multiple classes and variations of attributes from various categories, including demographic, academic, personal, and parental profiles. The implementation of five distinct machine learning models for prediction exploited a dataset sourced from the Kaggle repository. In order to mitigate attribute complexity across several categories, two approaches for attribute selection or reduction were employed. Furthermore, eight distinct metrics were employed for the examination of the models. The findings indicate that the classification model's performance in terms of accuracy was only average when considering multi-class predictions and variations of categorical attributes. This was observed after using attribute reduction approaches for 50% and 100% of the attributes.
Agrawal, R., & Sah, R. (2022). Multi-label Classification Methods and Challenges. International Journal of Mechanical Engineering, 7(4), 1295–1299.
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
Baashar, Y., Alkawsi, G., Ali, N., Alhussian, H., & Bahbouh, H. T. (2021). Predicting student’s performance using machine learning methods: A systematic literature review. Proceedings - International Conference on Computer and Information Sciences: Sustaining Tomorrow with Digital Innovation, ICCOINS 2021, 357–362. https://doi.org/10.1109/ICCOINS49721.2021.9497185
Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249–259. https://doi.org/10.1016/j.neunet.2018.07.011
Bydžovská, H. (2016). A Comparative Analysis of Techniques for Predicting Student Performance. International Educational Data Mining Society.
Deepika, K., & Sathyanarayana, N. (2018). Comparison of Student Academic Performance on Different Educational Datasets Using Different Data Mining Techniques. International Journal of Computational Engineering Research (IJCER), 8(9), 28–38.
Hall, M. A. (1999). Correlation-based Feature Selection for Machine Learning. April.
Idris, F., Hassan, Z., Ya’acob, A., Gill, S. K., & Awal, N. A. M. (2012). The Role of Education in Shaping Youth’s National Identity. Procedia - Social and Behavioral Sciences, 59, 443–450. https://doi.org/10.1016/j.sbspro.2012.09.299
Lango, M., & Stefanowski, J. (2022). What makes multi-class imbalanced problems difficult? An experimental study. Expert Systems with Applications, 199(January), 116962. https://doi.org/10.1016/j.eswa.2022.116962
Shapiro, L. (University of Washington). (2015). Information Gain Which test is more informative? https://homes.cs.washington.edu/$~$shapiro/EE596/notes/InfoGain.pdf
Mamoon-Al-Bashir, M., Kabir, M. R., & Rahman, I. (2016). The Value and Effectiveness of Feedback in Improving Students’ Learning and Professionalizing Teaching in Higher Education. Journal of Education and Practice, 7(16), 38–41. www.iiste.org
Molina, D., Poyatos, J., Ser, J. Del, García, S., Hussain, A., & Herrera, F. (2020). Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation, 12(5), 897–939. https://doi.org/10.1007/s12559-020-09730-8
Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Economic Review: Journal of Economics and Business, 10(1), 3–12.
ProjectPro (2023). How to Solve a Multi Class Classification Problem with Phyton. https://www.projectpro.io/article/multi-class-classification-python-example/547
Saleh, A. Y., & Liansitim, E. (2020). Palm oil classification using deep learning. Science in Information Technology Letters, 1(1), 1–8.
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
Adilah, S. M. Y., Venkat, I., Abdullah, R., & Yusof, U. K. (2011). Mass spectrometry analysis via metaheuristic optimization algorithms. Proceedings - 2011 6th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2011, 75–79. https://doi.org/10.1109/BIC-TA.2011.7
Tadese, M., Yeshaneh, A., & Mulu, G. B. (2022). Determinants of good academic performance among university students in Ethiopia: a cross-sectional study. BMC Medical Education, 22(1), 1–9. https://doi.org/10.1186/s12909-022-03461-0
Nedeva, V., & Pehlivanova, T. (2021). Students’ Performance Analyses Using Machine Learning Algorithms in WEKA. IOP Conference Series: Materials Science and Engineering, 1031(1), 0–13. https://doi.org/10.1088/1757-899X/1031/1/012061
Yılmaz N., Sekeroglu B. (2020) Student Performance Classification Using Artificial Intelligence Techniques. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://www.kaggle.com/datasets/csafrit2/higher-education-students-performance-evaluation
Yohannes, E., & Ahmed, S. (2018). Prediction of Student Academic Performance using Neural Network, Linear Regression and Support Vector Regression: A Case Study. International Journal of Computer Applications, 180(40), 39–47.
https://doi.org/10.5120/ijca2018917057
Yusoff, S. A. M., Abdullah, R., & Venkat, I. (2014). Adapted bio-inspired artificial bee colony and differential evolution for feature selection in biomarker discovery analysis. Advances in Intelligent Systems and Computing, 287, 111–120. https://doi.org/10.1007/978-3-319-07692-8_11
(Yusoff et al., 2024)
Yusoff, S. A. M., Othman, J., Mydin, A. M., Mohamad, W. A. W., Johan, E. J., & Yusoff, A. F. M. (2024). Students’ Academic Performance: Prediction using Machine Learning Approaches. International Journal of Academic Research in Progressive Education and Development, 13(3), 1778–1792.
Copyright: © 2024 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