Large-scale educational assessment data contains abundant information.Applying cognitive diagnosis methods to analyze such data can more effectively uncover individual cognitive processes and enhance the diagnostic function of assessments.Taking a large-scale science assessment in China as a case study,this study applied cognitive diagnosis analysis to students'response data by verifying the Q-matrix and selecting an appropriate cognitive diagnosis model.The results indicate that the Q-matrix,including both science content and science competence,is the most suitable model.Moreover,the saturated G-DINA model fits the large-scale assessment data better than other unsaturated models.In addition,students demonstrated a stronger mastery of science competence attribute,with significant differences observed across various mastery modes.This application of cognitive diagnosis methods to analyze large-scale assessments provided valuable insights for future research.
维普
万方数据