Learn and Evolve the Domain model in Intelligent Tutoring Systems: Approach based on Interaction traces
Résumé
In an online adaptive teaching system, the domain expert is not necessarily aware of the target audiences’ knowledge levels. Indeed, there could be a gap between what the domain expert thinks is the right way to organize the domain knowledge and how the domain knowledge should be organized to maximize the learners’ learning. In this context, we present a novel approach to fill this gap by the semi-automatic reorganization of the domain knowledge in a way that can potentially maximize students’ learning. We have developed the GOALS (Generator of Adaptive Learning Scenarios) platform that records the learners’ activities in the form of interaction traces. In this paper, we are interested in updating knowledge domain and learner profiles from the interaction traces. The results of the updating process are then presented to the domain expert who can approve or disapprove them accordingly. We will look for two
kinds of update, namely: 1) the detection of new concepts in the domain model, 2) the detection of new links between the domain concepts and the pedagogical resources. We apply mining algorithms to classify different students, according to their responses and then perform the analysis. We present our approach’s formalization, and some validations.