A Fuzzy Basis on Knowledge Structure Analysis for Cognition Diagnosis and Application on Geometry Concepts for Pupils

The main purpose of this study is to provide an integrated algorithm for knowledge structure analysis. This integrated algorithm combines item response theory (IRT), fuzzy logic model of perception (FLMP) and fuzzy structural modeling (FSM) so that the integrated algorithm could analyze individualized knowledge structure. Based on original response data and item-concept matrix, the individualized fuzzy subordinate matrix is acquired. FSM is applied in the fuzzy subordinate matrix so that the individualized knowledge structures provide information for cognition diagnosis. According to the empirical data analysis of geometry concept test for pupils, it shows that the integrated algorithm is an effective methodology for knowledge structure analysis and cognition diagnosis.