Optimization of Hyperparameters in Machine Learning for Enhancing Predictions of Student Academic Performance

Abstract

The prediction of student academic performance with high accuracy is of paramount importance in improving educational outcomes and developing tailored learning methodologies. It also serves as a preventative measure against student dropouts. This study is centered on enhancing the precision of such predictions by optimizing hyperparameters in machine learning techniques. In pursuit of optimal performance, a range of machine learning techniques is compared, and the most accurate one selected for hyperparameter optimization. The adopted method for this optimization is the Grid Search (GS) technique. It is found that hyperparameter optimization in the Gradient Boosting Regression Tree (GBRT) using the GS method bolsters the accuracy of predictions pertaining to student academic performance. The results obtained in this study are validated using a five-fold cross-validation method. This rigorous validation ensures the robustness of our findings. Thus, the study presents a critical contribution to the effective prediction of student academic performance, potentially informing the development of more efficient and personalized educational strategies.