Name
Abstract | ||
|---|---|---|
While the literature on learning at scale has largely focused on MOOCs, online degree programs, and AI techniques for supporting scalable learning experiences, informal learning communities have been relatively underrepresented. None-theless, these massive open online learning communities regularly draw far more engaged users than the typical MOOC. Their informal structure, however, makes them significantly more difficult to study. In this work, we take a first step toward attempting to understand these communi-ties specifically from the perspective of scale. Taking a sample of 62 such communities, we develop a tagging sys-tem for understanding the specific features and how they relate to scale. For example, just as a MOOC cannot man-ually grade every assignment, so also an informal learning community cannot approve every contribution; and just as MOOCs therefore employ autograding, informal learning communities employ crowd-sourced moderation or plat-form-driven enforcement. Using these tags, we then select several communities for deeper case studies. We also use these tags to make sense of learning-based subreddits from the popular community site Reddit, which offers an API for programmatic analysis. Based on these techniques, we offer findings about the performance of informal learning communities at scale and issue a call to include these envi-ronments more fully in future research on learning at scale.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3386527.3405926 | [email protected] '20: Seventh (2020) ACM Conference on Learning @ Scale
Virtual Event
USA
August, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7951-9 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Will Hudgins | 1 | 0 | 0.34 |
| Michael Lynch | 2 | 0 | 0.34 |
| Ash Schmal | 3 | 0 | 0.34 |
| Harsh Sikka | 4 | 0 | 0.34 |
| Michael Swenson | 5 | 0 | 0.34 |
| David Joyner | 6 | 9 | 8.40 |