Beyond collaborative filtering: Generating local top-N recommendations for personal learning environments

Abstract

In the field of personal learning environment (PLE) research is focusing on the generation and provision of recommendations. Amongst others, approaches reach from decision making tools based on psycho-pedagogical principles over specialized social recommender functionality up to general community or contextaware recommendations. The variety of the solutions results from the fact that pure collaborative filtering (CF) techniques are not sufficient for PLE-based scenarios. In this paper we propose utilizing learner interaction recordings for generating PLE recommendations fitting the educational and social context of a learner. Besides pointing out how we have realized this approach as part of a research prototype, we evaluate and discuss such recommendations generated from data captured in former studies.