Abstract
Exploratory Learning Environments allow learners to use different strategies for solving the same problem. However, not all possible strategies are known in advance to the designer or teacher and, even if they were, considerable time and effort would be required to introduce them in the knowledge base. We have previously proposed a learner modelling mechanism inspired from Case-based Reasoning to diagnose the learners when constructing or exploring models. This mechanism models the learners’ behaviour through simple and composite cases, where a composite case is a sequence of simple cases and is referred to as a strategy. This chapter presents research that enhances the modelling approach with an adaptive mechanism that enriches the knowledge base as new relevant information is encountered. The adaptive mechanism identifies and stores two types of cases: (a) inefficient simple cases, i.e. cases that make the process of generalisation more difficult for the learners, and (b) new valid composite cases or strategies.
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Cocea, M., Gutierrez-Santos, S., Magoulas, G.D. (2012). Case-Based Reasoning Approach to Adaptive Modelling in Exploratory Learning. In: Watanabe, T., Jain, L.C. (eds) Innovations in Intelligent Machines – 2. Studies in Computational Intelligence, vol 376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23190-2_12
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