Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at hom...Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress.To this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural networks.First,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.展开更多
文摘Educational data mining based on student cognitive diagnosis analysis can provide an important decision basis for personalized learning tutoring of students,which has attracted extensive attention from scholars at home and abroad and has made a series of important research progress.To this end,we propose a noise-filtering enhanced deep cognitive diagno-sis method to improve the fitting ability of traditional models and obtain students’skill mastery status by mining the interaction between students and problems nonlinearly through neural networks.First,modeling complex interactions between students and problems with multidimensional features based on cognitive processing theory can enhance the interpretability of the proposed model;second,the neural network is used to predict students’learning performance,diagnose students’skill mastery and provide immediate feedback;finally,by comparing the proposed model with several baseline models,extensive experimental results on real data sets demonstrate that the proposed Finally,by comparing the proposed model with several baseline models,the extensive experimental results on the actual data set demon-strate that the proposed model not only improves the accuracy of predicting students’learning performance but also enhances the interpretability of the neurocognitive diagnostic model.
