June 15, 2019
June 15, 2019
June 19, 2019
Computers in Education
Massive open online courses (MOOCs) provide an opportunity for motivated individuals to expand their education in ways previously unimaginable. However, for a variety of reasons, most MOOC learners stop engaging after interacting with only a fraction of the course, potentially for reasons related to the course itself. While it is well known that traditional educational metrics must be contextualized for the MOOC environment, there is little to guide instructors, instructional staff, or others regarding exactly how to make sense of the outcomes. Moreover, since different types of courses may attract different student populations with different behavior patterns, comparing learner behavior seen in specialized or advanced MOOCs with that seen in MOOCs generally may be unwarranted.
The resulting research question is, "In what ways do different types of learners interact differently with advanced STEM MOOCs?" In this study, we examined multiple advanced STEM MOOCs offered by nanoHUB on the edX platform. We first identified users’ behavioral patterns for each course through clustering how much learners interacted with the course. In each course, we found the proportion of students who belong to each cluster, the amount of each type of course material each cluster interacted with, and how recently students in each cluster engaged with the course. So far, across the courses, we see consistent patterns of engagement for each cluster as well as a consistent distribution of students within each cluster. Using these patterns, we have developed descriptions for five types of advanced STEM MOOC users.
Based on our findings, the next goal will be to create benchmarks for advanced STEM MOOCs by incorporating more courses into our analysis. Comparing course usage with similar courses could help us determine at what points learners’ behavior is typical and where it deviates from what is expected—perhaps indicating that the instructor should intervene before more learners depart. With these benchmarks, instructors running new courses or interventions will have baseline data to which they can compare their own students’ behavior.
Williams, T. V., & Douglas, K. A., & Bermel, P., & Merzdorf, H. E. (2019, June), Beyond the Means – Visualizing Learner Activity and Outcomes for Online Instructors Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/32152
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