A COMPARATIVE STUDY OF EXPLAINABLE MACHINE LEARNING MODELS FOR STUDENT ACADEMIC PERFORMANCE PREDICTION

Abstract

Student educational progress prediction has developed a serious examination area in ML, Academic Data mining and Explainable AI. Academic institution constantly pursue smart system to recognizing the risk students, in institution for decision making and refining personal education atmosphere. ML educational model predict the high analytical correctness, many model working as black-box system due to absence of transparency and understandability. This research openhanded a relative study of explainable Model for students education progress forecast. This investigates learning many ML algorithms having Random Forest, Decision Tree, SVM, Logistic Regression, XBM and XGBoost. Educational datasets covering attendance records, assignment scores, quiz marks, study hours, previous GPA, classroom participation, and demographic factors were used for testing. The Investigational results established that XGBoost attained the ultimate prediction accuracy of 93%, while Explainable Boosting Machine provided the excellent balance between predictive performance and interpretability. SHAP analysis used for identification of attendance, earlier GPA, assignment marks, as well as study time as the most significant features to influence the academic success.