A Pilot Study on Machine Learning Models for Predicting Student Pass/Fail Outcomes Using LMS Activity Logs and Interactive Dashboard Analytics
DOI:
https://doi.org/10.56705/ijodas.v7i2.415Keywords:
Machine Learning, Pass/Fail Prediction, Learning Management System, Educational Data Mining, Learning Analytics Dashboard, KDD FrameworkAbstract
Learning Management Systems (LMS) generate rich behavioral data from student interactions, yet many institutions utilize these data only for administrative purposes. This study develops a Machine Learning model for predicting student academic performance based on LMS activity logs and visualizes the predictions through an interactive dashboard. Following the Knowledge Discovery in Databases (KDD) process, data from LMS logs were selected, preprocessed, transformed with feature engineering, and modeled using Logistic Regression, Random Forest, and XGBoost. The Random Forest model achieved the highest performance with 87.0% accuracy and 0.92 F1-score. The interactive dashboard, implemented using Tableau, provides real-time visualizations, risk alerts and actionable recommendations for lecturers and academic administrators. This work contributes to Educational Data Mining and Learning Analytics by integrating predictive modeling with user-friendly visualization, supporting data-driven interventions in higher education.
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