Design Principles and Impact of a Learning Analytics Dashboard: Evidence from a Randomized MOOC Experiment

Learning Analytics Dashboards (LADs) are increasingly deployed to support self-regulated learning in large-scale online courses. Yet many existing dashboards lack strong theoretical grounding, contextual alignment, or actionable feedback, and some designs have been shown to inadvertently discourage learners through excessive social comparison or high inference costs. In this study, we designed and evaluated a LAD grounded in the COPES model of self-regulated learning and tailored to a credit-bearing Massive Open Online Course (MOOC) using a data-driven approach. We conducted a randomized controlled trial with 8,745 learners, comparing a control group, a dashboard without feedback, and a dashboard with ARCS-framed actionable feedback. Results showed that the dashboard with feedback significantly increased learners’ likelihood of verification (i.e., paying for the certification track), with mixed effects on engagement and no measurable impact on final grades. These findings suggest that dashboards are not uniformly beneficial: while feedback-supported LADs can enhance motivation and persistence, dashboards that lack interpretive support may impose cognitive burdens without improving outcomes. The study contributes to the learning analytics literature by (1) articulating design principles for theoretically and contextually grounded LADs, and (2) providing large-scale experimental evidence on their impact in authentic MOOC settings.