Using simulations to evaluate the effects of recommender systems for learners in informal learning networks

Abstract

Learning Networks consist of learners who are able to create, share and study learning activities. Through the emerging behaviour of such a network it may consist of a large amount of learning activities. Thus, the learners face the problem to select the most suitable learning activity regarding their learning goals in order to study the most efficient and effective learning path. This simulation study explores the use of recommender system technology like collaborative filtering to solve this problem. Learning activities that have been rated by comparable learners are recommended to the learners as navigational support. The simulation tool models a Learning Network in which learners search for, enrol in, study and rate learning activities. This article introduces our theoretical background for recommender systems in informal Learning Networks. It presents a model and flow chart of the simulation. It explains which collaborative filtering techniques we want to investigate and finally presents the experimental design for testing recommender systems in informal Learning Networks.