Learning Analytics for Student Performance and Early Detection of At-Risk

The rapid growth of online learning platforms has generated large volumes of student interaction data that may support learning analytics and early academic intervention. This study proposed an intelligent learning analytics system for predicting student performance and identifying at-risk students using online learning behavior data. The Online Learning Behavior Dataset was used, consisting of demographic information, learning environment variables, and behavioral indicators. Random Forest, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Gradient Boosting (XGBoost) models were implemented and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. SVM achieved the highest accuracy of 0.40, followed by Random Forest at 0.38, XGBoost at 0.35, and ANN at 0.32. However, because the task involved three risk categories, the best accuracy was only modestly above the approximate chance level of 0.33. These results indicate that the current models should be interpreted as exploratory decision-support tools rather than deployment-ready classifiers. The small performance differences among models matter for deployment because a marginal improvement may not justify automated risk classification unless supported by stronger validation, better feature engineering, and statistically meaningful performance gains. The study, therefore, demonstrates the potential of machine learning for exploratory learning analytics, while also emphasizing the need for verified institutional datasets and more rigorous evaluation before practical implementation.