Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for LatentSkill Discovering  

作　　者：Jing Geng Huali Yang Shengze Hu 

机构地区：[1]National Engineering Research Center of Educational Big Data,Central China Normal University,Wuhan,430079,China [2]Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan,430079,China [3]School of Computer Science and Artificial Intelligence,Wuhan Textile University,Wuhan,430079,China

出　　处：《Intelligent Automation & Soft Computing》2023年第8期1311-1324,共14页智能自动化与软计算（英文）

摘　　要：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.

关 键 词：Cognitive diagnosis nonlinear interaction INTERPRETABILITY intelligent education system skill diagnosis 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]