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

Autor: Peter Sloep, Kamakshi Rajagopal, Jan Van Bruggen
Přispěvatelé: RS-Research Line Teaching and Teacher Professionalisation (T2) (part of WO program), Department T2
Rok vydání: 2015
Předmět:
Zdroj: British Journal of Educational Technology, 48(2), 385-406. Wiley Blackwell
Rajagopal, K, Van Bruggen, J & Sloep, P 2017, ' Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown ', British Journal of Educational Technology, vol. 48, no. 2, pp. 385-406 . https://doi.org/10.1111/bjet.12366
ISSN: 1467-8535
0007-1013
Popis: 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. [ABSTRACT FROM AUTHOR]
Databáze: OpenAIRE
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