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May I Suggest? Comparing Three PLE Recommender Strategies

Author(s): Felix Mödritscher | Barbara Krumay | Sandy El Helou | Denis Gillet | Alexander Nussbaumer | Dietrich Albert | ingo Dahn | Carsten Ullrich

Journal: Digital Education Review
ISSN 2013-9144

Issue: 20;
Start page: 1;
Date: 2011;
Original page

Keywords: Personal learning environments | Recommender technology | Federated search | collaborative recommendations | Community-based recommendations | Psychopedagogical recommender | Technology review

Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general.
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