Redesigning teaching strategies through an information filtering system

Information filtering systems are responsible to eliminate useless or unnecessary information using computer-based techniques before being delivered to users. Recommender systems are active information filtering systems that attempt to present to the user information items in which s/he is interested. In this paper, we present an information filtering system and specifically a recommender system, which is incorporated in an authoring tool of an e-learning platform. The recommender system keeps records of the interaction between the students and the e-learning platform. More specifically, it takes into account the knowledge levels of students and models them using fuzzy weights. Moreover, it considers the educational objectives and models them using the Marzano's New Taxonomy. As such, the instructors receive refined hints and advice in order to redesign their teaching strategies and improve the knowledge acquisition of learners.