Comparing Peer Recommendation Strategies in a MOOC
Author(s) -
François Bouchet,
Hugues Labarthe,
Kalina Yacef,
Rémi Bachelet
Publication year - 2017
Publication title -
hal (le centre pour la communication scientifique directe)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/3099023.3099036
Subject(s) - attrition , computer science , recommender system , massive open online course , world wide web , random forest , multimedia , internet privacy , artificial intelligence , medicine , dentistry
International audienceLack of social relationship has been shown to be an important contribution factor for attrition in Massive Open Online Courses (MOOCs). Helping students to connect with other students is therefore a promising solution to alleviate this phenomenon. Following up on our previous research showing that embedding a peer recommender in a MOOC had a positive impact on stu-dents’ engagement in the MOOC, we compare in this paper the impact of three different peer recommenders: one based on so-cio-demographic criteria, one based on current progress made in the MOOC, and the last one providing random recommenda-tions. We report our results and analysis (N = 2025 students), suggesting that the socio-demographic-based recommender had a slightly better impact than the random one
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