Student Academic Performance Prediction Using Machine Learning

Abstract

The fast growth of educational data systems has led to more student data becoming available at scale to use in learning analytics. It is important to effectively analyze these data to forecast academic performance, as this will facilitate early detection of risks and individualized interventions. The present paper explores some of the most important determinants of academic achievement and assesses several predictive models based on the Student Performance Factors (SPF) dataset (N = 6,607). Linear Regression (LR) and Random Forest (RF) models are built and benchmarked against each other. As can be seen, the RF model, according to the results (R² = 0.70), is significantly outperforming the LR model (R² = 0.62), and the error rate has been minimized by 11.5 percent. Feature importance analysis indicates that attendance is the main determinant (importance weight = 0.381), followed by study hours (0.243) and past scores (0.091). Interestingly, the combination of the existing learning behaviors is six times greater than the historical performance, and the family background factors have insignificant direct effects. The results obtained can be used to justify data-driven educational interventions and imply that the monitoring of attendance should become the central element of any academic early warning system.