基于认知诊断模型PISA数据的学生学习轨迹分析  

Analysis and Exploration of Student Learning Trajectories in PISA Data Based on Cognitive Diagnostic Models

作　　者：崔爽 张兆远 张继超 CUI Shuang;ZHANG Zhaoyuan;ZHANG Jichao(School of Mathematics and Statistics,Yili Normal University,Yining 835000,China;Institute of Applied Mathematics,Yili Normal University,Yining 835000,China;Department of Mathematics,Changchun University of Finance and Economics,Changchun 130122,China)

机构地区：[1]伊犁师范大学数学与统计学院,新疆伊宁835000 [2]伊犁师范大学应用数学研究所,新疆伊宁835000 [3]长春财经学院数学教研部,吉林长春130122

出　　处：《长春师范大学学报》2024年第2期13-22,共10页Journal of Changchun Normal University

基　　金：伊犁师范大学2021年度博士科研启动基金项目“贝叶斯统计理论在教育学中的应用”(2021YSBS012);伊犁师范大学2023年度研究生教育教学改革研究项目“通过项目式教学改革提高研究生科研创新能力的探索与实践”(YS2023YG07)。

摘　　要：国际学生评估项目(PISA)在全球范围内为各国的教育水平评估提供了一个重要的横向比较平台,被广大学者认为是衡量教育质量的重要指标.近年来,重参数化DINA模型给出一种新的评估方法,能够进行更加准确的评估,逐渐在教育评估领域崭露头角,其独特之处在于能够结合认知分析把测量目标与认知过程模型紧密结合,展现学生对各知识点或技能的深入掌握程度.本文选择重参数化DINA模型作为分析工具,深入挖掘2015年PISA测试中来自中国、日本、美国等12个国家12918名学生的答题数据及反应时间,旨在明确这些国家学生的学习轨迹并对其进行对比.本文的研究结果呈现了不同国家学生认知学习过程的独特性,以期为国际教育改进提供有益的参考.PISA provides a crucial platform for cross-national comparisons of educational levels globally and is considered an important indicator of educational quality by scholars.Recently, the reparameterized DINA model has emerged as a novel assessment method, offering more accurate evaluations and gaining prominence in the field of educational assessment. This model uniquely combines measurement objectives with cognitive process models, reflecting students’ deep understanding of various knowledge points or skills. This paper utilizes the reparameterized DINA model to delve into the 2015 PISA test data, including response times of 12 918 students from 12 countries like China, Japan, and the USA. The aim is to clarify and compare the learning trajectories of students in these countries. The study’s findings reveal the distinct cognitive learning processes of students across different nations, providing valuable insights for international educational improvement.

关 键 词：DINA模型 PISA 学习轨迹 反应时间 

分 类 号：O212.8[理学—概率论与数理统计] G40-051[理学—数学]