Toward A Theoretical Basis for Dynamically Driven Content

The intent of dynamic learning with data driven content (DDC) in computer-mediated 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 one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated 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 follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model.