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Title: Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown
Authors: Rajagopal, Kamakshi
Van Bruggen, Jan
Sloep, Peter
Keywords: people recommenders
natural language processing
social networks
learning networks
Issue Date: 23-Dec-2015
Publisher: Wiley
Citation: Rajagopal, K., Van Bruggen, J. M., & Sloep, P. B. (2016, online available). Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown. British Journal of Educational Technology. doi: 10.1111/bjet.12366. Available online at
Abstract: People recommenders are a widespread feature of social networking sites and educational social learning platforms alike. However, when these systems are used to extend learners’ Personal Learning Networks, they often fall short of providing recommendations of learning value to their users. This paper proposes a design of a people recommender based on content-based user profiles, and a matching method based on dissimilarity therein. It presents the results of an experiment conducted with curators of the content curation site!, where curators rated personalized recommendations for contacts. The study showed that matching dissimilarity of interpretations of shared interests is more successful in providing positive experiences of breakdown for the curator than is matching on similarity. The main conclusion of this paper is that people recommenders should aim to trigger constructive experiences of breakdown for their users, as the prospect and potential of such experiences encourage learners to connect to their recommended peers.
Appears in Collections:1. T2 Publications, books and conference papers

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