Adapting learning paths in serious games: An approach based on teachers’ requirements

Abstract

Adapting Learning Paths in Serious Games (SGs) is a challenging problem. Indeed, learners are not alike; they have different range of abilities, competences, needs and interests. A well-fitting approach to create adaptive SGs is based on Competence-based Knowledge Space Theory (CbKST). CbKST allows sequencing the SG activities according to knowledge and competences of a domain model, and adaptation is based on suggesting activities that improve learners’ competences. However, differences among learners and the diversity of learning situations may drive teachers to consider implementing different adaptive approaches that fulfil their needs. In this work, we propose to use CbKST to enhance adaptation in SGs by considering not only the learner’s competence states but also teachers’ decisions based on their needs. More specifically, we have identified different needs concerning the possibility of advancing forward learning paths of SGs, as well as of reinforcing and deepening learners’ comprehension in specific subsets of competences. Therefore, we propose different recommendation strategies that allow teachers to modify the behaviour of adaptation in SGs, and we describe how we implemented and evaluated these strategies.