Bear cat: toward a theoretical basis for dynamically driven content in computer-mediated environments
Kathleen Scalise, Mark R. Wilson · 2004
This dissertation describes and investigates a new computer adaptive assessment approach for data driven content (DDC) in the UC Berkeley “Smart Homework” implementation of ChemQuery, an NSF-funded project. 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 individualized learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. In research presented in this paper, measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used to map knowledge spaces and drive computer-mediated learning environments with DDC. The paper presents BEAR (Berkeley Evaluation and Assessment Research) 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. As will be developed further here, the function of the measurement models applied is mathematically equivalent to the semi-linear neural net model. Research questions consider whether the iota model can serve as a valid and reliable item design to collect data and implement interactions in data-driven content, whether path scores through the testlet modeled to a cognitive framework can be considered equivalent, and how three testlet designs compare in fit and other measurement considerations.