A computational modeling of student cognitive processes in science education

摘要: The purpose of this paper is to explain and document the creation a computational model in form an Artificial Neural Network (ANN) capable simulating student cognition. Specifically, simulates students' cognition as they complete activities within science classroom. This study also seeks examine effects, evidenced ANN, intervention designed develop increased levels critical thinking related skills. based on identification cognitive attributes integration two advanced measurement frameworks: diagnostics Item Response Theory. Both frameworks response patterns, providing initial inputs for ANN portion model. Once task patterns are identified, parameterized presented ANN. foundational component upon interaction multiple, connected, adaptive processing elements know attributes. These process responses tasks tasks. Using Student Task Cognition Model (STAC-M), authors simulated training using randomized control trial design 100,000 students. Results simulation suggest that it possible increase success targeted attribute approach modeling provides means test educational theory future education research. discusses limitations use directions educators researchers. We successfully reasoning processes education.We underlying thought influence successful completion.Analysis suggestive gating attribute.Evaluation factors found STAC-M indicates similar real life.