Supporting Self-Regulated Personalised Learning through Competence-Based Knowledge Space Theory

Abstract

This article presents two current research trends in e-learning that at first sight appear to compete. Competence-Based Knowledge Space Theory (CBKST) provides a knowledge representation framework which, since its invention by Doignon & Falmagne, has been successfully applied in various e-learning systems (for example, Adaptive Learning with Knowledge Spaces [ALEKS] and Enhanced Learning Experience and Knowledge Transfer [ELEKTRA]), providing automated personalisation to learners' current knowledge and competence levels. Principles of self-regulated learning (SRL), pioneered by, for example, Zimmerman, however, argue for increased learner control, thus resulting in giving learners greater responsibility over their e-learning. The research presented in this article shows that skill-based visualisations in the tradition of CBKST and SRL-based autonomy are in no way conflicting but rather complement each other towards an integrated approach of self-regulated personalised learning. The research has been carried out and technologically translated into a set of visual tools for supporting the whole learning cycle within the scope of the iClass project.