Study on the Use of Random Forest Classifier and Multi-Output Classifier Models for Predicting Student Academic Performance and Identifying Areas of Concern

Abstract

Explores using machine learning to identify factors connected to student academic performance and to predict learning outcomes early, using two models: Random Forest classifier and Multi-Output classifier. Random Forest builds multiple decision trees and takes the mode of predictions, identifying the most significant factors. The Multi-Output classifier (neural-network backend) handles multi-label tasks, predicting several targets at once (e.g., grade and engagement level). Using several Kaggle student datasets, Random Forest identified key factors (units completed prior semester, prior-semester grades, tuition fee payment status) with 85.9% accuracy (94.5% correct on non-dropouts, 67.9% on dropouts). The Multi-Output Classifier yielded 83.5%–94.2% accuracy across five target variables — giving educators insight to advocate tailored support for at-risk students.