Interpretable Machine Learning Techniques for Students Grade Prediction

Abstract

Predicting student grades within an academic cycle is a remarkable opportunity to improve academic results. Robust prediction methods such as use of machine learning allow educational leaders to allocate enough resources and personalized instructions where necessary, fostering improved outcomes in Academia. We have presented various machine-learning models that have helped predict students' grades as an early intervention to determine how well the models achieve the required objectives using a combination of input features and past data or grades. We obtained a student dataset with various demographics, economic, educational factors, and grades from different subjects with their corresponding averages where we came up with 15 models including decision trees, random forest, linear regression, k-nearest neighbor, AdaBoost, Gradient Boosting, Support Vector Machine XGBoost, Lasso, Ridge, Elasticnet and among Deep Neural Networks. Our findings show that Linear Regression based model is the best with a MAPE of 8.14 and R-squared of 0.2536, and the Graph neural networks performed worst with a MAPE of 47.2 and R-squared of -83.7. We achieve computational outcomes indicative of the model's predictive performance. Our results show promising accuracy and generalization capabilities and that these models can predict very well, meaning we can rely on them to figure out how students might perform in academia.