PUB - Publikationen an der Universität Bielefeld

Intelligent tutoring systems (ITSs) typically rely on a formalised model of the underlying domain knowledge in order to provide feedback to learners adaptively to their needs. This approach implies two general drawbacks: the formalisation of a domain-specific model usually requires a huge effort, and in some domains it is not possible at all. In this paper, we propose feedback provision strategies in absence of a formalised domain model, motivated by example-based learning approaches. We demonstrate the feasibility and effectiveness of these strategies in several studies with experts and students. We discuss how, in a set of solutions, appropriate examples can be automatically identified and assigned to given student solutions via machine learning techniques in conjunction with an underlying dissimilarity metric. The plausibility of such an automatic selection is evaluated in an expert survey, while possible choices for domain-agnostic dissimilarity measures are tested in the context of real solution sets of Java programs. The quantitative evidence suggests that the proposed feedback strategies and automatic example assignment are viable in principle, further user studies in large-scale learning environments being the subject of future research.