[1]王洋,刘清堂,张文超,等.数据驱动下的在线学习状态分析模型及应用研究[J].远程教育杂志,2019,(02):074-80.[doi:10.15881/j.cnki.cn33-1304/g4.2019.02.009]
 Liu Qingtang,Wang Yang,Zhang Wenchao,et al.The Research on Online Learning State Analysis Model and Its Application Driven by Data[J].Distance Education Journal,2019,(02):074-80.[doi:10.15881/j.cnki.cn33-1304/g4.2019.02.009]
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数据驱动下的在线学习状态分析模型及应用研究()
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《远程教育杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2019年02期
页码:
074-80
栏目:
学习新论
出版日期:
2019-03-21

文章信息/Info

Title:
The Research on Online Learning State Analysis Model and Its Application Driven by Data
作者:
王洋;刘清堂;张文超; [美]David Stein
1.华中师范大学 信息化与基础教育均衡发展协同创新中心,湖北武汉 430079,2.俄亥俄州立大学 教育与人类生态学学院,美国俄亥俄,43210
Author(s):
Liu Qingtang; Wang Yang; Zhang Wenchao; David Stein
1. Collaborative & Innovative Center for Balanced Development of Information and foundation Education, Central China Normal University, Wuhan Hubei 430079; 2. College of Education and Human Ecology, the Ohio State University, Ohio U.S 43210
关键词:
数据驱动在线学习学习状态分析模型应用研究云课堂
Keywords:
Driven by DataOnline Learning Learning State Analytic Model Application ResearchCloud Classroom
分类号:
G420
DOI:
10.15881/j.cnki.cn33-1304/g4.2019.02.009
文献标志码:
A
摘要:
学习状态是决定学习者在线学习效果的重要指标。针对在线学习中学习者的学习状态的测量方法,探究学习者的学习状态与学习行为之间的关系,可以为教师、学习者和教育管理者提供评估、预测与干预的依据。基于“云平台”上269名学习者的12912条在线学习操作数据,构建的一个数据驱动的学习状态分析模型,对投入、挫折、困惑、分心四种学习状态的行为特征进行了分析。其精确度和科恩的一致性结果表明:机器分析结果与自我报告结果一致性较高,具有一定参考性。对其的进一步应用研究结果表明:学习者的学习状态与其学习效果显著相关,这为基于学习者学习行为数据的状态分析研究提供了参考。
Abstract:
Learning state is a determinate factor of students online learning. Aimed at analyzing the relationship among learning state, learning efficiency and learning behavior, this study constructs a measurement model of four learning states with the decision tree method: engagement, frustration, confusion and off-task, and verifies its effectiveness based on 12912 online learning operations from 269 students on the “Cloud Class” to provide evidence for teachers, learners and educational managers to evaluate, predict and intervene. The accuracy and kappa verified the consistency between the results of this analysis method and self-report in analyzing students’ learning state. Further application results demonstrated that learners’ learning state is significantly correlated with their learning effectiveness. This study provides a reference for learners’ state analysis based on their learning data.

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备注/Memo

备注/Memo:
基金项目:本文系湖北省技术创新专项“互联网+精准教育关键技术研究与示范”(2017ACA105)、教育部-中国移动科研基金项目“信息技术支持下的区域教研模式研究及试点”(MCM20170502)、华中师范大学优秀博士论文培育计划项目(2018YBZZ038)的研究成果。
更新日期/Last Update: 1900-01-01