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Friday, December 6 • 11:30am - 12:00pm
Enabling Resilient Massive Scale Open Online Learning Communities through Models of Social Emergence (30m)

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One important hurdle that prevents MOOCs from reaching their transformative potential is that they fail to provide the kind of social environment that is conducive to sustained engagement and learning, especially as students arrive in waves to these online learning communities. The unique developmental history of MOOCs creates challenges that require insight into the inner-workings of massive scale social interaction in order to meet. Early attempts to organize the community into smaller study groups may be thwarted by such periodic growth spurts paired with attrition, as groups that initially had an appropriate critical mass soon fall below that level and then are unable to support the needs of remaining students. This proposed research seeks to lay the foundation for meeting this challenge through computational modeling of massive scale social interaction in order to yield new knowledge about the inner-workings of interaction in such environments so that support for healthy community formation can be designed and built. The ultimate goal of the eventual fully automated and context sensitive support would be to yield more resilient massive scale online learning environments. The methodological goal of the research is to contrast alternative computational modeling techniques including traditional social network analysis, simple topic modeling through Latent Dirichlet Allocation (LDA), and more sophisticated graphical modeling approaches that integrate these two lenses in a powerful way. This new approach is referred to as the Tensor Based Mixed Membership Stochastic Block Model, or tensor based MMSB.
In this MRI project, PI Rosé is partnering with colleagues at the University of Pittsburgh’s Institute for Learning (IFL) who are launching a MOOC on Accountable Talk through Coursera starting in September 2013 as well as Prof. Eric P. Xing of Carnegie Mellon University’s Machine Learning Department who specializes in large scale Graphical Modeling approaches to massive scale data analysis.


Friday December 6, 2013 11:30am - 12:00pm
Meeting Room 11

Attendees (8)