Title
Toward A Theoretical Basis for Dynamically Driven Content: ComputerMediated Environments and Personalised eLearning,Used
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The intent of dynamic learning with data driven content (DDC) in computermediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional onesizefitsall applications. Measurement technologies similar to some models underlying computeradaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computermediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate "pathway" parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and followup probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semilinear neural net model.
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