Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown

Kamakshi Rajagopal, Jan Van Bruggen, Peter Sloep

Research output: Contribution to journalArticleAcademicpeer-review

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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 Scoop.it!, 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.
Original languageEnglish
Pages (from-to)385-406
Number of pages22
JournalBritish Journal of Educational Technology
Volume48
Issue number2
Early online date23 Dec 2015
DOIs
Publication statusPublished - Mar 2017

Keywords

  • people recommenders
  • natural language processing
  • breakdown
  • social networks
  • learning networks

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