It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to d...It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and o</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">r</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning. Four results were found. Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently. Secondly, the complication of the structures will decrease the accuracy of the classification. The order of the influence is linear, convergent, unstructured and divergent. It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures. Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results. At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.展开更多
本研究应用规则空间模型（Rule Space Model，RSM）诊断参加《实用汉语水平认定考试C．TEST）的学生的考试结果，通过对852名被试在C.TEST[A—D级]测验18个听力理解题目上的作答反应进行分析，将97％左右的被试成功地归入了68种属性掌...本研究应用规则空间模型（Rule Space Model，RSM）诊断参加《实用汉语水平认定考试C．TEST）的学生的考试结果，通过对852名被试在C.TEST[A—D级]测验18个听力理解题目上的作答反应进行分析，将97％左右的被试成功地归入了68种属性掌握模式。在此基础上，为被试提供了关于其听力理解技能掌握情况的诊断性报告，为教师和测验设计者提供了关于其教学情况和试题研发情况的诊断及反馈信息。展开更多
文摘It is very important to identify the attribute mastery patterns of the examinee in cognitive diagnosis assessment. There are many methods to classify the attribute mastery patterns and many studies have been done to diagnose what the individuals have mastered and o</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">r</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> Montel Carl Computer Simulation is used to study the classification of the attribute mastery patterns by Deep Learning. Four results were found. Firstly, Deep Learning can be used to classify the attribute mastery patterns efficiently. Secondly, the complication of the structures will decrease the accuracy of the classification. The order of the influence is linear, convergent, unstructured and divergent. It means that the divergent is the most complicated, and the accuracy of this structure is the lowest among the four structures. Thirdly, with the increasing rates of the slipping and guessing, the accuracy of the classification decreased in verse, which is the same as the existing research results. At last, the results are influenced by the sample size of the training, and the proper sample size is in need of deeper discussion.
文摘本研究应用规则空间模型（Rule Space Model，RSM）诊断参加《实用汉语水平认定考试C．TEST）的学生的考试结果，通过对852名被试在C.TEST[A—D级]测验18个听力理解题目上的作答反应进行分析，将97％左右的被试成功地归入了68种属性掌握模式。在此基础上，为被试提供了关于其听力理解技能掌握情况的诊断性报告，为教师和测验设计者提供了关于其教学情况和试题研发情况的诊断及反馈信息。
