Personalised Learning Environments based on Knowledge Graphs and the Zone of Proximal Development

Abstract

The learning of new knowledge and skills often requires previous knowledge, which can lead to some frustration if a teacher does not know a learner’s exact knowledge and skills and therefore confronts them with exercises that are too difficult to solve. We present a solution to address this issue when teaching techniques and skills in the domain of table tennis, based on the concrete needs of trainers that we have investigated in a survey. We present a conceptual model for the representation of knowledge graphs as well as the level at which individual players already master parts of this knowledge graph. Our fine-grained model enables the automatic suggestion of optimal exercises in a player’s so-called zone of proximal development, and our domain-specific application allows table tennis trainers to schedule their training sessions and exercises based on this information. In an initial evaluation of the presented solution for personalised learning environments, we received positive and promising feedback from trainers. We are currently investigating how our approach and conceptual model can be generalised to some more traditional educational settings and how the personalised learning environment might be further improved based on the expressive concepts of the presented model.