Evaluation of Machine Learning Models in Student Academic Performance Prediction
This work addresses student performance prediction for educational institutions, but it is incremental as it uses existing methods on new data.
This research tackled the problem of predicting student academic performance by applying machine learning models to behavioral, academic, and demographic data, achieving a maximum test accuracy of 86.46% with a multi-layer perceptron classifier.
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.