KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting
This work addresses the issue of knowledge retention for learners in e-learning environments, though it appears incremental as it builds on existing recommendation methods.
The paper tackles the problem of forgetting in e-learning by developing KnowledgeCheckR, which uses recommendation techniques to predict and suggest relevant learning units for repetition, and demonstrates its applicability through empirical studies in real-world scenarios.
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.