Early Engagement Analysis for Dropout Prediction in Large-Scale Online Learning Platforms

Abstract

Online education platforms generate extensive learning behavior data that can support personalized instruction and curriculum optimization. However, transforming raw interaction logs into actionable insights remains challenging. This study analyzes activity records from an online learning platform serving over 620,000 students across secondary and vocational education programs. Metrics including video completion rates, quiz attempts, and forum participation were correlated with final assessment outcomes. Results indicate that early engagement patterns within the first three weeks predict course completion with approximately 73% accuracy. Intervention strategies based on these indicators reduced dropout rates by 9.6% in pilot courses. The findings demonstrate the potential of learning analytics for improving educational outcomes, while also raising concerns regarding data governance and student privacy.