A Pilot Study on Machine Learning Models for Predicting Student Pass/Fail Outcomes Using LMS Activity Logs and Interactive Dashboard Analytics - Pratiwi Rachmadi (yocto brain, 2026)
Introduction: Learning Management Systems (LMS) generate extensive behavioral data that can support early identification of students at risk of academic failure, yet such data are often underutilized for predictive and intervention purposes. Method: This exploratory pilot study developed a machine learning pipeline following the Knowledge Discovery in Databases (KDD) framework using LMS activity logs from 36 students enrolled in a Discrete Mathematics course. Student-level behavioral features were extracted and modeled using Logistic Regression, Random Forest, and XGBoost. Model performance was evaluated using repeated 5-fold cross-validation, while an interactive learning analytics dashboard was designed to translate predictions into risk alerts and actionable recommendations for lecturers. Results and Discussion: Random Forest achieved the best overall performance with 87.0% accuracy, 0.88 precision, 0.94 recall, a macro-averaged F1-score of 0.92, and an AUC of 0.89, outperforming XGBoost and Logistic Regression. Assignment regularity and average quiz scores were identified as the most influential predictors. Qualitative feedback from two lecturers indicated that integrating risk information within a familiar LMS interface could support more practical and timelier student intervention. However, the small single-course sample limits generalizability. Conclusion: The proposed approach demonstrates the feasibility of integrating machine learning prediction and learning analytics dashboards as an early-warning mechanism, while further validation using larger, multi-course, and multi-institutional datasets is required.