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In online collaborative learning environments, students create content andconstruct their own knowledge through complex interactions over time. Tofacilitate effective social learning and inclusive participation in thiscontext, insights are needed into the correspondence betweenstudent-contributed artifacts and their subsequent popularity among peers. Inthis study, we represent student artifacts by their (a) contextual action logs(b) textual content, and (c) set of instructor-specified features, and usethese representations to predict artifact popularity measures. Through amixture of predictive analysis and visual exploration, we find that the neuralembedding representation, learned from contextual action logs, has thestrongest predictions of popularity, ahead of instructor's knowledge, whichincludes academic value and creativity ratings. Because this representation canbe learnt without extensive human labeling effort, it opens up possibilitiesfor shaping more inclusive student interactions on the fly in collaborationwith instructors and students alike.

Unsupervised Representations Predict Popularity of Peer-Shared Artifacts in an Online Learning Environment