The Future of Education

Massive Open Online Courses (MOOCs) scale access to learning, yet instructors and platforms struggle to translate noisy, sparse engagement traces into timely support that is both pedagogically meaningful and feasible to deliver. This paper proposes a three-stage, workload-aware intervention framework that bridges learning analytics outputs and course-level decision making. Stage 1 derives interpretable early-warning signals from engagement trajectories and assessment participation (e.g., inactivity accumulation, engagement variability, and assessment-to-content imbalance). Stage 2 maps these signals into risk archetypes via transparent rules that resolve conflicting evidence and prioritize actionable explanations (e.g., early disengagers, deadline-driven cramming, irregular persistence, and assessment-heavy strategies). Stage 3 operationalizes archetypes under capacity constraints by selecting policies that explicitly balance expected benefit against available instructional resources, enabling stepped-care targeting (universal low-intensity support plus budgeted, targeted higher-intensity supports when risk persists). We use the HarvardX–MITx Person–Course dataset [1] to illustrate that many MOOC learners generate very little activity data, so effective support should be delivered in levels: low-cost help for everyone and more intensive interventions only for a limited number of learners who show strong, persistent risk signals. The contribution is a practitioner-oriented blueprint for designing interventions that remain faithful to evidence in learner traces while acknowledging the operational realities of large-scale online education [2-3].