A recommender system for educational resources in specific learning contexts

This paper presents the algorithms that drive the behaviour of a recommendation system for educational resources in specific learning contexts. The criteria taken into account to provide these recommendations is not based on the preferences or previous behaviour of an individual (personalization). Instead of that, we need to consider the particular characteristics of a learning context: subject, language, tools available, age range, etc. We present three different multi-criteria recommendation algorithms depending upon the type of resource to be recommended: tools, events and contributors (experts, parents and other external potential contributors to a learning activity). These algorithms have been designed as a joint effort between technical and pedagogical experts within a large European project: innovative Technologies for an Engaging Classroom (iTEC).