KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting

TL;DR

This paper presents KnowledgeCheckR, an e-learning environment that employs predictive recommendation techniques to counteract forgetting by identifying and repeating relevant learning units, supported by empirical validation.

Contribution

It introduces integrated recommendation approaches for predicting and reinforcing relevant learning content to mitigate forgetting in e-learning environments.

Findings

Utility-based recommendation identifies key content for repetition.

Collaborative filtering supports session-based learning recommendations.

Empirical studies validate the effectiveness of the techniques.

Abstract

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.