John Scott Co-Authors Paper on Popularity of Peer-Shared Artifacts in Online Learning Environments

John Scott Co-Authors Paper on Popularity of Peer-Shared Artifacts in Online Learning Environments

John Scott with Renzhe Yu and Zach Pardos, co-authored the paper, 'Unsupervised Representations Predict Popularity of Peer-Shared Artifacts in Online Learning Environments', which earned an honorable mention for best paper submission at the Conference on Educational Data Science 2020.

This paper main argues:

In online collaborative learning environments, students create content and construct their own knowledge through complex interactions over time. To facilitate effective social learning and inclusive participation in this context, insights are needed into the correspondence between student-contributed artifacts and their subsequent popularity among peers. In this study, we represent student artifacts by their (a) contextual clickstream of interactions (b) textual content, and (c) set of instructor-specified features, and use these representations to predict artifact popularity scores. Through a mixture of predictive analysis and visual exploration, we find that the neural embedding representation, learned from contextual clickstream, has the strongest predictions of popularity, ahead of instructor's knowledge, which includes academic value and creativity ratings. Because this representation can be learnt without human labeling, it opens up potential possibilities for shaping student interactions towards the more inclusive and pedagogically valuable on the fly.

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