Learning Analytics for Massive Open Online Courses

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Massive Open Online Courses (MOOCs) have arisen as an increasingly popular way to deliver quality educational content to individual learners. On the delivery platform, much data can be gathered and analyzed about the learners, including demographics, discussion postings, assessment submissions, course progress events, and course marks. With growing amounts of data for many thousands of learners, the proposed research program aims to provide timely advice that identifies learning difficulties, and to offer suggestions that personalize the learning experience. Popular today with learners interested in professional development are MOOC specializations, each a cohesive sequence of self-paced courses followed by a scheduled capstone project course. Most of these learners have a degree, work in industry, have families, and must use their time effectively. From our experience in designing, constructing, producing, and delivering a MOOC specialization on software product management, we have a special opportunity to better understand and assist our learners, with aims to reduce any frustrations and increase engagement. While learning through a MOOC is often individualistic, we see a budding community of practice through the discussion forums. The proposed research explores incentives to encourage better contributions and sharing, to provide a richer learning experience that improves course quality. This research program seeks to develop effective concepts, methods, and tools to understand learners and their activities, to help improve learning, foster communities of practice, boost completion rates, and promote engagement.