Overcoming the MOOC Data Deluge with Learning Analytic Dashboards

• Published in: Learning Analytics: Fundaments, Applications, and Trends Vol. 94; pp. 171 - 198
• Main Authors: Vigentini, Lorenzo, Clayphan, Andrew, Zhang, Xia, Chitsaz, Mahsa
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Studies in Systems, Decision and Control
• Subjects: Artificial intelligence; Dashboards; Educational equipment & technology, computer-aided learning (CAL); Learning and teaching data; MOOCs; Sense-making; Teaching skills & techniques; Visualization
• ISBN: 9783319529769; 3319529765
• ISSN: 2198-4182; 2198-4190
• DOI: 10.1007/978-3-319-52977-6_6

With the proliferation of MOOCs and the large amount of data collected, a lot of questions have been asked about their value and effectiveness. One of the key issues emerging is the difficulty in the sense—making from the data available. The use of analytic dashboards has been suggested to provide quick insights and distil the large volume of learner interaction data generated. These dashboards hold the promise of providing a contextualized view of data and facilitating useful research exploration. However, little has been done in defining how these dashboards should be created, often resulting in a proliferation of systems for each new research agenda. We present our experience of building MOOC dashboards for two different platforms, namely Coursera and FutureLearn, motivated by a set of design goals with input from a diverse set of stakeholders. We demonstrate the features of the system and how it has served to make data accessible and useable. We report on problems faced, drawing on analyses of think-aloud sessions conducted with real educators, which have informed our dashboard process.