[1]马 华,黄卓轩*,唐文胜.面向个性化学习的认知诊断模型及其应用综述[J].计算机技术与发展,2021,31(11):35-40.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 006]
　MA Hua,HUANG Zhuo-xuan*,TANG Wen-sheng.Review on Cognitive Diagnosis Model and Its Application forPersonalized Learning[J].,2021,31(11):35-40.[doi:10. 3969 / j. issn. 1673-629X. 2021. 11. 006]
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面向个性化学习的认知诊断模型及其应用综述()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年11期
页码:
35-40
栏目:
大数据分析与挖掘
出版日期:
2021-11-10

文章信息/Info

Title:
Review on Cognitive Diagnosis Model and Its Application forPersonalized Learning
文章编号:
1673-629X(2021)11-0035-06
作者:
马 华; 黄卓轩* ; 唐文胜
湖南师范大学 信息科学与工程学院,湖南 长沙 410081
Author(s):
MA Hua; HUANG Zhuo-xuan* ; TANG Wen-sheng
School of Information Science and Engineering,Hunan Normal University,Changsha 410081,China
关键词:
个性化学习; 在线学习; 认知诊断模型; 应用研究; 综述
Keywords:
personalized learning; online learning; cognitive diagnosis model; application research; review
分类号:
TP331
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 11. 006
摘要:
近年来,随着在线学习的大规模普及与应用,各类在线学习平台上已经累积了海量的学生学习相关的历史数据,这为开展教育数据挖掘提供了重要的研究基础。 基于学生认知诊断分析的教育数据挖掘可为学生的个性化学习辅导提供重要的决策依据,目前已经吸引了国内外相关学者的广泛关注,并取得了一系列重要的研究进展。 通过对面向在线个性化学习的认知诊断模型的研究现状进行综述,阐述了项目反应理论和 DINA( deterministic inputs, noisy and-gate) 模型的特点和不足,介绍了多种改进的 DINA 模型以及其他有代表性的认知诊断模型。 并且分析了当前认知诊断模型在个性化辅助学习中的典型应用领域及代表性成果,典型应用领域包括学生考试成绩的预测、个性化学习资源推荐和协同学习小组构建等。 最后,总结全文工作并对面向在线个性化学习的认知诊断模型研究的未来方向进行了展望。
Abstract:
With the large-scale popularization and application of online learning recently,the mass of historical data related to students and their learning have been accumulated on online learning platforms,which can provide an important research basis for educational data mining. The educational data mining based on cognitive diagnosis analysis can provide the important decision - making basis for personalized learning guidance,and has attracted extensive attention from relevant scholars at home and abroad. A series of important research advances have been made. The research progress of cognitive diagnosis models ( CDMs) for the online personalized learning is reviewed. The characteristics and shortcomings of the item response theory and DINA ( deterministic inputs,noisy and-gate) model are explained,and the improved DINA models and other representative CDMs are summarized. Furthermore,the classical application fields and their representative research on CDMs in personalized learning are elaborated. These application fields include examine performance prediction,personalized learning resource recommendation,collaborative learning group formation,etc. Finally,the work is summarized and the future research directions of CDMs in personalized learning are discussed.

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更新日期/Last Update: 2021-11-10