Learning with personalized recommender systems: A psychological view

This paper explores the potentials of recommender systems for learning from a psychological point of view. It is argued that main features of recommender systems (collective responsibility, collective intelligence, user control, guidance, personalization) fit very well to principles in the learning sciences. However, recommender systems should not be transferred from commercial to educational contexts on a one-to-one basis, but rather need adaptations in order to facilitate learning. Potential adaptations are discussed both with regard to learners as recipients of information and learners as producers of data. Moreover, it is distinguished between system-centered adaptations that enable proper functioning in educational contexts, and social adaptations that address typical information processing biases. Implications for the design of educational recommender systems and for research on educational recommender systems are discussed.

► Recommender systems must be adapted in order to be applied to learning scenarios.
► Recommender systems must be tailored to learner knowledge and learning activities.
► Recommender systems must be persuasive; they should challenge beliefs.
► Recommender systems should be based on explicit ratings.
► Recommender systems should stress social visibility.