User Modelling and Adaptation in Exploratory Learning

User modelling in Exploratory Learning Environments (ELEs) is an emerging field with several challenges to be addressed. Due to the freedom given to learners, the amount of information generated is very large, making the modelling process very challenging. Consequently, only relevant information should be used in the user modelling process. This, however, leads to other challenges such as identification of relevant information, finding an optimal knowledge representation and defining an inference mechanism by which this knowledge is used in diagnosing the learner. This thesis addresses the challenges of user modelling in ELEs by monitoring learners’ behaviour and taking into account only relevant actions in the context of an ELE for the domain of mathematical generalisation. An iterative approach was used, in line with the iterative design of the ELE. The modelling mechanism employed a modified version of Case-based Reasoning (CBR) and was evaluated using pedagogical scenarios and data from simulated and real students. This approach has the advantage of storing only relevant information and allows learner diagnosis during as well as at the end of a task. The user model was further exploited to support learning related activities, such as prioritising feedback and grouping for collaboration. For feedback prioritisation, a mechanism based on Multi-criteria Decision Making was developed and tested with the help of educational experts. The grouping for collaboration approach was inspired from Group Technology, a method from cellular manufacturing systems, and its testing showed it produces meaningful groups. Both the feedback prioritisation and the grouping for collaboration mechanisms propose solutions that are particularly relevant for ELEs by considering pertinent criteria for this type of learning. To ensure optimal coverage of the knowledge base, the user modelling approach was enhanced with adaptive mechanisms for expanding the knowledge base, which was tested on real and simulated data. This approach ensures that learner diagnostic is possible when the initial knowledge base is small and/or new behaviours are encountered over time.