EXPLORING THE ACCURACY AND RELIABILITY OF MACHINE LEARNING APPROACHES FOR STUDENT PERFORMANCE
Bilal OWAIDAT
bilal_owaydat@hotmail.comLebanese International University (Lebanon)
https://orcid.org/0000-0002-4176-2197
Abstract
The purpose of this study is to examine the suitability of machine learning (ML) techniques for predicting students’ performance. By analyzing various ML algorithms, the authors assess the accuracy and reliability of these approaches, considering factors such as data quality, feature selection, and model complexity. The findings indicate that certain ML methods are more effective for student performance forecasting, emphasizing the need for a deliberate evaluation of these factors. This study provides significant contributions to the field of education and reinforces the growing use of ML in decision-making and student performance prediction.
Keywords:
Classification, Regression, Student Performance, Machine LearningReferences
Adane, M. D., Deku, J. K., & Asare, E. K. (2023). Performance analysis of Machine Learning algorithms in prediction of student academic performance. Journal of Advances in Mathematics and Computer Science, 38(5), 74-86. https://doi.org/10.9734/jamcs/2023/v38i51762
Google Scholar
Agrawal, H., & Mavani, H. (2015). Student performance prediction using machine learning. International Journal of Engineering Research and Technology, 4(3), 111–113. http://dx.doi.org/10.17577/IJERTV4IS030127
Google Scholar
Ahajjam, T., Moutaib, M., Aissa, H., Azrour, M., Farhaoui, Y., & Fattah, M. (2022). Predicting students’ final performance using Artificial Neural Networks. Big Data Mining and Analytics, 5(4), 294-301. https://doi.org/10.26599/BDMA.2021.9020030
Google Scholar
Alghamdi, A. S., & Rahman, A. (2023). Data mining approach to predict success of secondary school students: A Saudi Arabian case study. Education Sciences, 13(3), 293. https://doi.org/10.3390/educsci13030293
Google Scholar
Altabrawee, H., Ali, O., & Qaisar, A. (2019). Predicting students’ performance using machine learning techniques. Journal of University of Babylon for Pure and Applied Sciences, 27(1), 194-205. https://doi.org/10.29196/jubpas.v27i1.2108
Google Scholar
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
Google Scholar
Chai, T., & Draxler, R. R. (2014). Root mean square error (rmse) or mean absolute error (mae)?–arguments against avoiding rmse in the literature. Geoscientific model development, 7(3), 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
Google Scholar
He, T. (2015). Xgboost: Extreme gradient boosting. Data Camp. https://rdocumentation.org/packages/xgboost/versions/0.4-2
Google Scholar
Chen, Y., & Zhai, L. (2023). A comparative study on student performance prediction using machine learning. Education and Information Technologies, 28, 12039-12057. https://doi.org/10.1007/s10639-023-11672-1
Google Scholar
Demir, K., & Güraksın, G. E. (2022). Determining middle school students’ perceptions of the concept of artificial intelligence: A metaphor analysis. Participatory Educational Research, 9(2), 297-312. https://doi.org/10.17275/per.22.41.9.2
Google Scholar
Fayoumi, A. G., & Hajjar, A. F. (2020). Advanced learning analytics in academic education: Academic performance forecasting based on an artificial neural network. International Journal on Semantic Web and Information Systems, 16(3), 70-87. https://doi.org/10.4018/IJSWIS.2020070105
Google Scholar
Freund, R. J., Wilson, W. J., & Sa, P. (2006). Regression analysis. Elsevier.
Google Scholar
Ghorbani, R., & Ghousi, R. (2020). Comparing different resampling methods in predicting students’ performance using machine learning techniques. IEEE Access, 8, 67899–67911. https://doi.org/10.1109/ACCESS.2020.2986809
Google Scholar
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classifi- cation: an overview. ArXiv, abs/2008.05756. https://doi.org/10.48550/arXiv.2008.05756
Google Scholar
Gull, H., Saqib, M., Iqbal, S. Z., & Saeed, S. (2020). Improving learning experience of students by early prediction of student performance using machine learning. 2020 IEEE International Conference for Innovation in Technology (INOCON) (pp. 1-4). IEEE. https://doi.org/10.1109/INOCON50539.2020.9298266
Google Scholar
Harvey, J. L., & Kumar, S. A. P. (2019). A practical model for educators to predict student performance in k-12 education using machine learning, 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 3004-3011). IEEE. https://doi.org/10.1109/SSCI44817.2019.9003147
Google Scholar
Kingsford, C., & Salzberg, S. (2008). What are decision trees? Nature Biotechnology, 26, 1011-1013. https://doi.org/10.1038/nbt0908-1011
Google Scholar
Kukkar, A., Mohana, R., Sharma, A., & Nayyar, A. (2023). Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms. Education and Information Technologies, 28, 9655-9684. https://doi.org/10.1007/s10639-022-11573-9
Google Scholar
McDonald, G. C., (2009). Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 93-100. https://doi.org/10.1002/wics.14
Google Scholar
Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7(21). https://doi.org/10.3389/fnbot.2013.00021
Google Scholar
Onyema, E. M., Almuzaini, K. K., Onu, F. U., Verma, D., Gregory, U. S., Puttaramaiah, M., & Afriyie, R. K. (2022). Prospects and challenges of using machine learning for academic forecasting. Computational Intelligence and Neuroscience, 2022(1), 5624475. https://doi.org/10.1155/2022/5624475
Google Scholar
Oyedeji, A. O., Salami Olaolu, A. M., & Abolade, F. O. R. (2020). Analysis and prediction of student academic performance using machine learning. Journal of Information Technology and Computer Engineering, 4(1), 10–15. https://doi.org/10.25077/jitce.4.01.10-15.2020
Google Scholar
Ranstam, J., Cook, J. A. (2018). Lasso regression. British Journal of Surgery, 105(10), 1348. https://doi.org/10.1002/bjs.10895
Google Scholar
Salas Rueda, R. A., De la cruz Martínez, G., Eslava Cervantes, A. L., Castañeda Martínez, R., & Ramírez Ortega, J. (2022). Teachers’ opinion about collaborative virtual walls and massive open online course during the COVID-19 pandemic. Online Journal of Communication and Media Technologies, 12(1), e202202. https://doi.org/10.30935/ojcmt/11305
Google Scholar
Schulz, E., Speekenbrink, M., & Krause, A. (2018). A tutorial on gaussian pro- cess regression: Modelling, exploring, and exploiting functions. Journal of Mathematical Psychology, 85, 1–16. https://doi.org/10.1016/j.jmp.2018.03.001
Google Scholar
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). Student performance prediction and classification using machine learning algorithms. 8th International Conference on Educational and Information Technology (pp. 7-11). Association for Computing Machinery. https://doi.org/10.1145/3318396.3318419
Google Scholar
Su, X., Yan, X., & Tsai, C. L. (2012). Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 275-294. https://doi.org/10.1002/wics.1198
Google Scholar
Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from vle big data using deep learning models. Computers in Human Behavior, 104, 106189. https://doi.org/10.1016/j.chb.2019.106189
Google Scholar
Authors
Bilal OWAIDATbilal_owaydat@hotmail.com
Lebanese International University Lebanon
https://orcid.org/0000-0002-4176-2197
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