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Title: Simulating light-weight Personalised Recommender Systems in Learning Networks: A case for Pedagogy-Oriented and Rating-based Hybrid Recommendation Strategies
Authors: Nadolski, Rob
Van den Berg, Bert
Berlanga, Adriana
Drachsler, Hendrik
Hummel, Hans
Koper, Rob
Sloep, Peter
Keywords: recommendation strategy
simulation study
collaborative filtering
Issue Date: 3-Apr-2008
Abstract: Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Sound way-finding for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS) which should also be practically feasible with minimized effort. Currently, such light-weight PRS systems are scarcely available. This study shows that simulations can support defining PRS requirements prior to starting the costly process of development, implementation, testing, revision, and before conducting field experiments with real learners. This study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this simulation study reveals that a rating-based light-weight hybrid PRS-system is a good alternative for ontology-based recommendations, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).
Description: Nadolski, R. J., Van den Berg, B., Berlanga, A. J., Drachsler, H., Hummel, H. G. K., Koper, R., & Sloep, P. B. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation 12(1)4 <>.
Appears in Collections:1. LN: Publications and Preprints
Keur der Wetenschap
1. TENC: Publications and Preprints

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