Journal article Open Access

Collaboration-Type Identification in Educational Datasets

Waters, Andrew; Studer, Christoph; Baraniuk, Richard

Identifying collaboration between learners in a course is an important challenge in education for two reasons: First, depending on the courses' rules, collaboration can be considered a form of cheating. Second, it helps one to more accurately evaluate each learner's competence. While such collaboration identification is already challenging in traditional classroom settings consisting of a small number of learners, the problem is greatly exacerbated in the context of both online courses or massively open online courses (MOOCs) where potentially thousands of learners have little or no contact with the course instructor. In this work, we propose a novel methodology for collaboration-type identification, which both identifies learners who are likely collaborating and also classifies the type of collaboration employed. Under a fully Bayesian setting, we infer the probability of learners' succeeding on a series of test items solely based on graded response data. We then use this information to jointly compute the likelihood that two learners were collaborating and what collaboration model (or type) was used. We demonstrate the efficacy of the proposed methods on both synthetic and real-world educational data; for the latter, the proposed methods find strong evidence of collaboration among learners in two non-collaborative takehome exams.

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