面向学习测评的纵向认知诊断模型的泛化性能比较研究  

The Comparative Study on the Generalization Performance of Longitudinal Cognitive Diagnostic Models for Learning Assessment

作　　者：宋丽红[1] 袁思玉 汪文义[2] SONG Lihong;YUAN Siyu;WANG Wenyi(School of Education,Jiangxi Normal University,Nanchang Jiangxi 330022,China;School of Computer and Information Engineering,Jiangxi Normal University,Nanchang Jiangxi 330022,China)

机构地区：[1]江西师范大学教育学院,江西南昌330022 [2]江西师范大学计算机信息工程学院,江西南昌330022

出　　处：《江西师范大学学报（自然科学版）》2023年第4期384-392,共9页Journal of Jiangxi Normal University(Natural Science Edition)

基　　金：国家自然科学基金(62267004,62067005,61967009);江西省高等学校教学改革研究课题(JXJG-22-2-44)资助项目.

摘　　要：为了研究纵向认知诊断模型适应新数据的能力,该文主要考查3种纵向认知诊断模型在不同类型纵向数据上的泛化性能.这3种纵向认知诊断模型分别为模式级别上的潜在转换分析模型Patt-DINA、属性级别上的潜在转换分析模型Att-DINA和基于高阶潜在结构的sLong-DINA模型.借助被试知识状态的属性判准率、模式判准率、绝对拟合指标和相对拟合指标等4个指标,评价这3种模型的表现.研究结果表明:Att-DINA模型和sLong-DINA模型在大多数条件下更具优势,即泛化性能相对较好,Patt-DINA模型因待估计参数较多而优势较小,但Patt-DINA模型在样本量较大时仍具有优势并且它能估计的知识状态类间转移概率有更大的变化空间.To investigate the ability of longitudinal cognitive diagnostic models to adapt to fresh data,the generalization performance of three longitudinal cognitive diagnostic models on different types of longitudinal data is investigated.The first longitudinal cognitive diagnostic model is Patt-DINA,a model of latent transition analysis at the attribute pattern level.The second is the Att-DINA,a model of latent transition analysis at the attribute level.And the sLong-DINA model is based on higher-order latent structures.The performance of these three models is evaluated with the correct classification rates of attribute and pattern of students′knowledge states,the absolute model fit index and the relative model fit index.The results of the simulation study show that the Att-DINA model and the sLong-DINA model are more advantageous in most conditions,which means that their generalization performance is relatively better.The Patt-DINA model is less advantageous due to the larger number of parameters to be estimated,but the model still has advantages when the sample size is large and it can estimate transition probabilities of knowledge states with more space for variation.

关 键 词：纵向认知诊断模型 属性转换 模式转换 高阶模型 泛化性能 

分 类 号：B841[哲学宗教—基础心理学]