Abstract
| Original language | English |
|---|---|
| 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 |
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Keywords
- people recommenders
- natural language processing
- breakdown
- social networks
- learning networks
Cite this
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Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown. / Rajagopal, Kamakshi; Van Bruggen, Jan; Sloep, Peter.
In: British Journal of Educational Technology, Vol. 48, No. 2, 03.2017, p. 385-406.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Recommending peers for learning: Matching on dissimilarity in interpretations to provoke breakdown
AU - Rajagopal, Kamakshi
AU - Van Bruggen, Jan
AU - Sloep, Peter
PY - 2017/3
Y1 - 2017/3
N2 - 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.
AB - 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.
KW - people recommenders
KW - natural language processing
KW - breakdown
KW - social networks
KW - learning networks
U2 - 10.1111/bjet.12366
DO - 10.1111/bjet.12366
M3 - Article
VL - 48
SP - 385
EP - 406
JO - British Journal of Educational Technology
JF - British Journal of Educational Technology
SN - 0007-1013
IS - 2
ER -