Design Principles and Impact of a Learning Analytics Dashboard: Evidence from a Randomized MOOC Experiment
Name
applsci-15-11493.pdf
Size
3.28 MB
Format
Adobe PDF
Checksum (MD5)
95ecf4dfb253ba8694843c8fe5648166
Author(s)
Borrella, Inma
Ponce-Cueto, Eva
Date Issued
October 27, 2025
Journal
Applied Sciences
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Borrella, I., & Ponce-Cueto, E. (2025). Design Principles and Impact of a Learning Analytics Dashboard: Evidence from a Randomized MOOC Experiment. Applied Sciences, 15(21), 11493.
Version
Final published version
Abstract
Learning Analytics Dashboards (LADs) are increasingly deployed to support self-regulated learning on 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 8745 learners, comparing a control group, a dashboard without feedback, and a dashboard with ARCS-framed actionable feedback. The 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. This study contributes to the literature on learning analytics by (1) articulating the design principles for theoretically and contextually grounded LADs and (2) providing experimental evidence on their impact in authentic MOOC settings.
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/app152111493
