This is like having an AI ‘teaching assistant’ quietly watching how students interact with digital lessons—how often they log in, what they click, how long they stay focused—and then turning that into a clear picture of who is engaged, who is struggling, and which activities actually work best.
Educators and institutions struggle to measure and improve student engagement in digital and blended learning. Engagement data is fragmented across LMS logs, quizzes, and content tools, and instructors rarely have time or analytics skills to interpret it. This solution automates the analysis of learning traces to identify engagement levels and patterns so that interventions can be timely and data‑driven.
Access to longitudinal student interaction data and institutional LMS logs, plus course- and context-specific engagement labels, can create proprietary models and benchmarks that are hard for generic tools to copy. Tight embedding into curriculum design and teaching workflows also creates switching costs.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Data privacy and governance around sensitive student data; integrating and normalizing heterogeneous LMS and learning tool logs at scale.
Early Majority
This work focuses specifically on quantifying and modeling ‘engagement’ from detailed learner interaction traces (clickstreams, activity logs, assessments) in an educational setting, rather than generic learning analytics dashboards. It emphasizes engagement-level prediction, early warning signals, and pedagogical insight rather than pure reporting or generic BI.