This paper considers the problem of shape-based recognition and pose estimation of 3-D free-form objects in

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Personalized e-learning allows the course creator to create courseware that dynamically adapts to the needs of individual learners or learner groupings. This dynamic nature of adaptive courseware makes it difficult to evaluate what the delivery time courseware will be for the learner. The course creator may attempt to validate adaptive courseware through dummy runs, but cannot eliminate the risk of pedagogical problems due to adaptive courseware's inherent variability. Courseware validation checks whether adaptive courseware conforms to a set of pedagogical and nonpedagogical requirements. The validation of adaptive courseware limits the risk of pedagogical problems at delivery time. In this paper, we present our approach to adaptive courseware validation using the Courseware Authoring Validation Information Architecture (CAVIAr). We outline how CAVIAr captures adaptive courseware authoring concerns and validates courseware using a constraint-based approach. We also describe how CAVIAr can be integrated with the state of the art in adaptive e-learning and evaluate our validation approach.

A lack of pedagogy in courseware can lead to learner rejection. It is therefore vital that pedagogy is a central concern of courseware construction. Courseware validation allows the course creator to specify the pedagogical rules and principles that courseware must conform to. In this paper we investigate the information needed to automate courseware validation and propose an information architecture to be used as a basis for validation. We then demonstrate an approach to courseware validation in the context of the information architecture presented.