This paper identifies computational challenges in restructuring encyclopedic resources (like Wikipedia or thesauri) to reorder concepts with the goal of helping learners navigate through a concept network without getting trapped in circular dependencies between concepts. We present approaches that can help content authors identify regions in the concept network, that after editing, would have maximal impact in terms of enhancing the utility of the resource to learners.

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A competence guided casebase maintenance algorithm retains a case in the casebase if it is useful to solve many problems and ensures that the casebase is highly competent. In this paper, we address the compositional adaptation process (of which single case adaptation is a special case) during casebase maintenance by proposing a case competence model for which we propose a measure called retention score to estimate the retention quality of a case. We also propose a revised algorithm based on the retention score to estimate the competent subset of a casebase. We used synthetic datasets to test the effectiveness of the competent subset obtained from the proposed model. We also applied this model in a tutoring application and analyzed the competent subset of concepts in tutoring resources. Empirical results show that the proposed model is effective and overcomes the limitation of footprint-based competence model in compositional adaptation applications.

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