Higher Education and Predictive Analytics: Assessing Student Performance with Artificial Intelligence
Keywords:
higher education, predictive analytics, artificial intelligence, academic performance, machine learning, educational data mining, learning analytics
Abstract
This article analyzes the use of artificial intelligence (AI) to anticipate academic performance in higher education through the development of a predictive model based on machine learning. The study adopts a quantitative, explanatory approach grounded in data science techniques, using an anonymized dataset of 5,500 university students. A Random Forest model was built and validated, achieving an accuracy of 87% and an AUC-ROC of 0.91. Key predictors included GPA, assignment submission rates, LMS access frequency, and forum participation. SHAP analysis was applied to ensure model transparency, and the student population was segmented into three academic risk levels. Findings demonstrate that AI can be successfully integrated into educational management systems to enable early diagnosis, personalized interventions, and improvements in student retention. Ethical considerations, limitations, and future research directions are discussed.Downloads
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References
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Sweeney, M., Lester, J., & Rangwala, H. (2021). Students matter the most in learning analytics: The effects of internal and instructional factors on academic performance. Computers & Education, 170, 104225. https://doi.org/10.1016/j.compedu.2021.104225
Zhou, M., Zhang, M., & Huang, Y. (2020). Predicting student performance with data-driven features in blended learning environments. Computers in Human Behavior, 105, 106215. https://doi.org/10.1016/j.chb.2019.106215
Albrechtsen, D., Conati, C., & Roy, M. (2021). Predicting academic success using behavioral data in online learning environments. Journal of Educational Data Mining, 13(2), 27–48. https://doi.org/10.5281/zenodo.4774707
Aljohani, N. R., Memon, M. H., Alshamrani, Y., & Tariq, M. (2023). Accurate, timely, and portable: Course-agnostic early prediction of student performance. Computers and Education: Artificial Intelligence, 4, 100145. https://doi.org/10.1016/j.caeai.2023.100145
Baker, R. S. (2010). Data mining for education. In B. McGaw, E. Baker, & P. Peterson (Eds.), International Encyclopedia of Education (Vol. 7, pp. 112–118). Elsevier. https://doi.org/10.1016/B978-0-08-044894-7.01223-8
Baker, R. S. J. d. (2010). Data mining for education. International Encyclopedia of Education, 7, 112–118. https://doi.org/10.1016/B978-0-08-044894-7.01223-8
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
Cabrera, E., Páez, D., & González, C. (2021). Análisis predictivo en educación: una revisión desde el machine learning. Revista Iberoamericana de Tecnología Educativa, 11(1), 56–70.
Kaur, H., Sharma, P., & Singh, M. (2023). Student course grade prediction using the random forest algorithm. Education and Information Technologies, 28(1), 123–139. https://doi.org/10.1007/s10639-023-11300-1
Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 1(3), 111–120. https://doi.org/10.1016/j.procs.2010.12.015
Lakkaraju, H., Aguiar, E., Shan, C., Miller, D., Bhanpuri, N., Ghani, R., & Addison, K. (2015). A machine learning framework to identify students at risk of adverse academic outcomes. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1909–1918. https://doi.org/10.1145/2783258.2788620
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
Sweeney, M., Lester, J., & Rangwala, H. (2021). Students matter the most in learning analytics: The effects of internal and instructional factors on academic performance. Computers & Education, 170, 104225. https://doi.org/10.1016/j.compedu.2021.104225
Zhou, M., Zhang, M., & Huang, Y. (2020). Predicting student performance with data-driven features in blended learning environments. Computers in Human Behavior, 105, 106215. https://doi.org/10.1016/j.chb.2019.106215
Published
2025-01-21
How to Cite
Dasuki, J. (2025). Higher Education and Predictive Analytics: Assessing Student Performance with Artificial Intelligence. IJMSOR: International Journal of Management Science & Operation Research, 10(1), 98-112. Retrieved from https://ijmsoridi.com/index.php/ijmsor/article/view/138
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