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Research on inspectable learner models involves the study of representational aspects of the profile that systems maintain about learners. When learners and teachers interact with inspectable learner models several questions arise, such as: What kind of representation is most appropriate for learners and teachers?; How will students and teachers react to different representations?; How should learners and teachers interact with the model?; In order to answer these and other questions, we have developed ViSMod, an interactive visualization tool for Bayesian learner models and ConceptLab, a knowledge construction and navigation system that uses XML-based conceptual maps to represent the learner’s view of the domain.