Improve Performance of Recommender System in Collaborative Learning Environment based on Learner Tracks

Qing Tang, Marie-Hélène Abel, Elsa Negre

Abstract

Learning with huge amount of open educational resources is challenging, especially when variety resources come from different System of Information Systems (SoIS). How to help learners obtain appropriate resources efficiently in collaborative learning environment is still a rigorous problem of research. This paper proposes a method to calculate learner’s knowledge competency by tracking and analyzing their behaviors in a collaborative learning environment based on SoIS, and combining other basic learner’s information to build a personalized recommender system to help learners select appropriate educational resources to improve their learning efficiency.

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