Evaluation of Machine Learning Models in Student Academic Performance Prediction

TL;DR

This study evaluates various machine learning models, especially neural networks, for predicting student academic performance, highlighting the importance of feature selection and explainability in model performance.

Contribution

It demonstrates the effectiveness of neural networks like MLPC in student performance prediction and emphasizes the role of feature selection and explainability techniques.

Findings

MLPC achieved up to 86.46% accuracy on test data.

Feature selection significantly improved model performance.

Explainable ML methods helped validate feature importance.

Abstract

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach.