  Existing e-learning environments primarily focus on the aspect of providing
intuitive learning contents and to recommend learning units in a personalized
fashion. The major focus of the KnowledgeCheckR environment is to take into
account forgetting processes which immediately start after a learning unit has
been completed. In this context, techniques are needed that are able to predict
which learning units are the most relevant ones to be repeated in future
learning sessions. In this paper, we provide an overview of the recommendation
approaches integrated in KnowledgeCheckR. Examples thereof are utility-based
recommendation that helps to identify learning contents to be repeated in the
future, collaborative filtering approaches that help to implement session-based
recommendation, and content-based recommendation that supports intelligent
question answering. In order to show the applicability of the presented
techniques, we provide an overview of the results of empirical studies that
have been conducted in real-world scenarios.
