Recommender system of educational resources: a critiquing-based proposal

Abstract

Resumen: The learning objects recommending systems designed in the last decade employ different recommendation techniques, which mainly use collaborative filtering and contentbased filtering. This paper aims to analyze a new recommendation approach, that has not been yet applied to learning objects communities, called critiquing-based recommendation. This approach gathers the users preferences through an interaction based on examples and critiques. This feedback helps the system to recommend the most suitable learning resources. In this work we propose an hybrid recommender system of open educational resources that combines collaborative filtering and critiquing-based techniques. A prototype using MERLOT data and considering just one round of interaction was built in order to evaluate the recommendation results amongst a small group of volunteers. Preliminary results pointed out that the relevance of recommended learning resources increases as the system receives the users feedback and generates new recommendations based on it.