Semantics in Educational Recommender Systems

Abstract

Recommender systems are information filtering systems that rank items according to user interests and preferences. They are widely used on online shopping sites like Amazon and Netflix, but have also become increasingly popular in educational contexts and on e-learning platforms. Many recent recommender systems employ various semantic techniques to increase the accuracy of the systems or enrich the user experience in general.

The purpose of this study is to investigate the requirement of Semantics in Educational Recommender Systems and to survey the existing recommender systems for education and e-learning. This study analyzes how recommender systems can be applied to current e-learning systems to guide learners in a personalized way by including e-learning scenarios and a framework providing the classication of the recommender system based on the underlying technology and educational purpose.

Keywords: Semantic recommendation, Educational Recommender Systems, Semantics in Educational Recommender Systems, Semantics Recommender Systems, Emotions, Technology enhanced learning, E-learning services.