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贝叶斯网在认知诊断属性层级结构确定中的应用

期刊名称：心理学报
时间：0
页码：338-346
语言：中文
分类：B841[哲学宗教—基础心理学;哲学宗教—心理学]
作者机构：[1]江西师范大学计算机信息工程学院,南昌330027, [2]安徽亳州师范高等专科学校计算机系,亳州233500, [3]江西省南昌市第一中学,南昌330003
相关基金：国家自然科学基金项目（编号30860084和60263005）; 全国教育考试“十一五”科研规划课题（编号2009JKS2009）; 教育部人文社科项目（编号09YJCXLX012 10YJCXLX049）; 安徽省2010年度高校省级自然科学研究项目（编号KJ2010B123）
相关项目：认知诊断评估模型开发及应用

关键词：规则空间模型, 认知诊断, 贝叶斯网, 结构学习, rule space model, cognitive diagnosis, Bayesian networks, structure learning

中文摘要：

K.K.Tatsuoka和她同事研究的规则空间模型（RSM）是一种在国内外有较大影响的认知诊断模型,但是Tatsuoka的RSM是由学科专家先从已编制的测验中抽取出属性,然后给出测验的关联Q阵,再由该Q阵导出属性间的层级关系。已有研究证明,这种做法所得到的属性间的层级关系难以保证是正确的,甚至难以保证属性间的层级关系是唯一确定的。这里利用贝叶斯网进行结构学习,从被试的属性掌握模式中挖掘出属性间的层级关系,学习所得到的层级关系可以用来验证由RSM中的方法得到的层级关系。模拟实验和实证研究的结果都显示了该方法所得到的属性层级关系是有参考价值的,可以为命题或测量专家带来有用的信息。

英文摘要：

It＇s very significant to correctly identify the hierarchical relation among attributes correctly when constructing a diagnosis test.As we know,there are various methods to identify the hierarchical relation among attributes in different cognitive diagnosis models（CDMs）.Rule Space Model（RSM） is a kind of great influence CDM which was developed by Tatsuoka and her associates.In RSM,the task of attribute identification is performed after the test items have already been developed.And then an incidence Q matrix can be determined which reflects hierarchical relation.However,in Leighton,Gierl and Hunka＇s（2004） Attribute Hierarchy Method（AHM） the organization of attributes（attributes,number of attributes and attribute hierarchical relation） should be determined before developing the test items.In RSM and AHM the importance of correctly identifying the attributes and their hierarchical relation cannot be overstated,and the attributes and their hierarchical relations serve as the most important input variables to the models because they provide the basis for interpreting the results.The hierarchical relation among attributes describes the domain knowledge structures.Understanding the domain knowledge structures in highly specific detail provides a rational basis for proposing and evaluating potential improvements in the measurement of general proficiency.Otherwise,improvement remains largely a trial-and-error process.It has been proved that is not reliable to get the hierarchical relation by analyzing the test items,even the resulting hierarchical relations are not unique.Now,structure learning of Bayesian Networks would be introduced to study the hierarchical relation from the examinees＇ attribute mastery patterns.On the one hand,this paper conducted a simulation study in which Cui,Leighton and Zheng（2006）＇s attribute hierarchical relation was employed,and the sample size of 5000 was used.In the simulated study,different sliping rates was considered for the purpose of testing the robustnes

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