Dissertation PDF Available

Machine Learning Approaches for Predicting Academic Performance in Philippine Universities

2022 · College of Information and Computing Sciences

Archived: July 2026

Maria Santos · et al.

Abstract

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This research develops and evaluates machine learning models for early prediction of student academic performance to enable timely interventions.

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CICS

College of Information and Computing Sciences

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Keywords:

machine learning academic performance prediction Random Forest early warning student retention

References

Alamri, R., Alharbi, B., & Alshehri, M. (2021). Predicting student academic performance using machine learning: A systematic review. Education and Information Technologies, 26(4), 4067–4090. https://doi.org/10.1007/s10639-021-10498-x

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., & Liao, S. N. (2018). Predicting academic performance: A systematic literature review. Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 175–199.

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), Article e1355.

Publication Information

College: College of Information and Computing Sciences
Category: Dissertation
Publication Year: 2022
Date Archived: Jul 2026

Authors

M
Maria Santos
E
et al.

College

CICS

College of Information and Computing Sciences