Spreading Expertise Scores in Overlay Learner Models

Intelligent tutoring systems adapt learning resources depending on learners’ models. Successful adaptation is largely based on comprehensive and accurate learner models. By exploiting the network structure of ontology overlay models, we infer new learner knowledge and calculate the knowledge level we refer to as expertise scores. This paper presents a novel score propagation algorithm using constrained spreading activation and heuristics based on relative depth scaling. The algorithm spreads expertise scores amongst topics in a learner’s overlay model. We compared this novel approach with a baseline algorithm in the domain of programming languages and asked human experts to evaluate the calculated scores. Our results suggest that the novel algorithm tends to calculate more accurate expertise scores than the baseline approach.