Student Academic Performance Prediction Using Machine Learning: A Comparative Study of Decision Tree, Random Forest, and K-Nearest Neighbor
DOI:
https://doi.org/10.67710/appliedengineering.v1i2.7Keywords:
Educational Data Mining, Machine Learning, Random Forest, Decision Tree, K-Nearest NeighborAbstract
Forecasting how well students will perform has become a major theme in educational data mining (EDM), because spotting learners who are likely to struggle gives institutions time to step in before difficulties become entrenched. This narrative review draws together evidence from 17 peer-reviewed articles published between 2021 and 2026 that evaluated Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers on a variety of educational datasets. Where a clear ranking was possible, RF finished first or joint-first more often than either rival, a result the reviewed authors typically attribute to the way averaging many trees curbs overfitting and captures non-linear interactions among features. DT, however, matched or exceeded RF when the features were clean and strongly predictive, and it remains the easiest of the three to explain, whereas KNN swung widely with data quality, class balance, and feature scaling. Three methodological gaps recur across the corpus: models are rarely validated on data from other institutions, fairness across student groups is seldom examined, and the tension between accuracy and interpretability remains unresolved.
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Copyright (c) 2026 Isa Anita, Restu Ramadhan Syafiani, Tarisya Alifiya Zahra (Author)
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