Comparing Peer Recommendation Strategies in a MOOC

François Bouchet; Hugues Labarthe; Kalina Yacef; Rémi Bachelet

Comparing Peer Recommendation Strategies in a MOOC

François Bouchet ¹ Hugues Labarthe ^{1, 2} Kalina Yacef ³ Rémi Bachelet ⁴

1 MOCAH - Modèles et Outils en ingénierie des Connaissances pour l'Apprentissage Humain

LIP6 - Laboratoire d'Informatique de Paris 6

2 FRAMESPA - France, Amériques, Espagne – Sociétés, pouvoirs, acteurs

3 The University of Sydney [Sydney]

4 Ecole Centrale de Lille

Abstract : Lack 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.

Keywords : clustering recommendation strategies MOOC attrition peer recommendation

Document type :

Conference papers

Domain :

Computer Science [cs] / Technology for Human Learning

Computer Science [cs] / Artificial Intelligence [cs.AI]

Computer Science [cs] / Human-Computer Interaction [cs.HC]

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https://hal.archives-ouvertes.fr/hal-01531503
Contributor : François Bouchet <>
Submitted on : Tuesday, February 12, 2019 - 12:05:11 PM
Last modification on : Wednesday, September 18, 2019 - 1:30:54 AM
Long-term archiving on : Monday, May 13, 2019 - 2:03:56 PM

Bouchet et al. - 2017 - Compar...

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