KNOWLEDGECHECKR: Intelligent Techniques for Counteracting Forgetting

Existing e-learning environments primarily focus on theaspect of providing intuitive learning contents and to recommendlearning units in a personalized fashion. The major focus of theKNOWLEDGECHECKR environment is to take into account forget-ting processes which immediately start after a learning unit has beencompleted. In this context, techniques are needed that are able to pre-dict which learning units are the most relevant ones to be repeated infuture learning sessions. In this paper, we provide an overview of therecommendation approaches integrated in KNOWLEDGECHECKR.Examples thereof areutility-based recommendationthat helps toidentify learning contents to be repeated in the future,collaborativefilteringapproaches that help to implement session-based recommen-dation, andcontent-based recommendationthat supports intelligentquestion answering. In order to show the applicability of the pre-sented techniques, we provide an overview of the results of empiricalstudies that have been conducted in real-world scenarios