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 language | English |
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Pages (from-to) | 385-406 |
Number of pages | 22 |
Journal | British Journal of Educational Technology |
Volume | 48 |
Issue number | 2 |
Early online date | 23 Dec 2015 |
DOIs | |
Publication status | Published - Mar 2017 |
Keywords
- people recommenders
- natural language processing
- breakdown
- social networks
- learning networks