From Reactive Grading to Proactive Intervention: Building a Behavioral Early Warning System for Online Learners

In online and distance education, the ability to identify at-risk students early is essential for improving retention and academic success. This study develops a Behavioral Early Warning System (EWS) using clickstream data from the Open University Learning Analytics Dataset (OULAD), which includes 32,593 students and over 10.6 million interactions across 22 module presentations. The research introduces a multi-dimensional behavioral framework focusing on four key dimensions: Consistency (Gini Study Distribution), Intensity (average clicks per active day), Total Effort, and Spacing (time between sessions), supplemented by academic context variables. Using binary logistic regression, the full-dataset baseline model achieved 74.92% accuracy with a strong recall of 0.81 for failing students. When tested with limited temporal windows to simulate real-world deployment, the model reached 62.6% accuracy using only the first 30 days of data and improved to 68.7% by day 150. The findings demonstrate that temporal patterns of student engagement are detectable early enough to support proactive intervention. Interestingly, the Gini Study Distribution variable exhibited a positive relationship with student success, suggesting that moderate and strategically concentrated engagement patterns may reflect adaptive self-regulated learning behaviors rather than purely ineffective cramming. In contrast, excessively high-intensity study sessions were associated with elevated academic risk. These results highlight the importance of not only how much students engage, but also how they distribute and structure their effort over time. Overall, the study provides a practical, interpretable, and scalable framework for institutions seeking to shift from reactive grading to timely and data-driven student support in online learning environments.