Abstract
Recognition of peer learning as a valuable supplement to formal education has lead to a rich literature formalising peer learning as an institutional resource. Facilitating peer learning support sessions alone however, without providing guidance or context, risks being ineffective in terms of any targeted, measurable effects on learning. Building on an existing open-source, student-facing platform called RiPPLE, which recommends peer study sessions based on the availability, competencies and compatibility of learners, this paper aims to supplement these study sessions by providing content from a repository of multiple-choice questions to facilitate topical discussion and aid productiveness. We exploit a knowledge tracing algorithm alongside a simple Gaussian scoring model to select questions that promote relevant learning and that reciprocally meet the expectations of both learners. Primary results using synthetic data indicate that the model works well at scale in terms of the number of sessions and number of items recommended, and capably recommends from a large repository the content that best approximates a proposed difficulty gradient.
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Evaluated as the probability that a random score under a standard normal distribution is greater than the midpoint between two sequential items, (\(\frac{d}{2}\)).
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Potts, B.A., Khosravi, H., Reidsema, C. (2018). Reciprocal Content Recommendation for Peer Learning Study Sessions. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_34
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