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因素越多,理解越佳:大规模开放在线课程中探究社区的模型验证与结构效度研究

More factors, better understanding: model verification and construct validity study on the community of inquiry in MOOC.

作者信息

Bai Xuemei, Gu Xiaoqing, Guo Rifa

机构信息

Department of Educational Technology, School of Education, Ningxia University, 217 North Wencui Street, Yinchuan, China.

Department of Educational Information Technology, East China Normal University, 3663 North Zhongshan Road, Shanghai, China.

出版信息

Educ Inf Technol (Dordr). 2023 Jan 26:1-24. doi: 10.1007/s10639-023-11604-z.

Abstract

This study aimed to verify the applicability of the community of inquiry (CoI) survey instrument in MOOC involving 1,186 college students from 11 different disciplines in China. Exploratory factor analysis was used to explore potential factor structure models, and confirmatory factor analysis was utilized to verify the four-factor structure obtained from exploratory factor analysis. The original three- and new six-factor structure models were also included in the study. Confirmatory factor analysis results indicating that all three models fit very well with the data. Then Chi-square difference test was used to select the optimal model. Results indicate that the six-factor structure model with teaching presence, social presence, cognitive presence, design and organization, affective expression, and resolution is the optimal one, with good convergent and discriminant validity. Especially, the chi-square difference results indicate that design and organization can be significantly distinguished from teaching presence, whereas affective expression can be significantly distinguished from social presence, and resolution can be significantly distinguished from cognitive presence. Based on these findings, the present study argues that the six-factor structure model can provide a better understanding for the fine design and implementation of MOOC.

摘要

本研究旨在验证探究社区(CoI)调查工具在中国11个不同学科的1186名大学生参与的大规模在线开放课程(MOOC)中的适用性。采用探索性因素分析来探索潜在的因素结构模型,并利用验证性因素分析来验证从探索性因素分析中获得的四因素结构。原始的三因素和新的六因素结构模型也纳入了本研究。验证性因素分析结果表明,所有三个模型都与数据拟合得很好。然后使用卡方差异检验来选择最优模型。结果表明,具有教学临场感、社会临场感、认知临场感、设计与组织、情感表达和问题解决的六因素结构模型是最优模型,具有良好的聚合效度和区分效度。特别是,卡方差异结果表明,设计与组织可以与教学临场感显著区分开来,情感表达可以与社会临场感显著区分开来,问题解决可以与认知临场感显著区分开来。基于这些发现,本研究认为六因素结构模型可以为MOOC的精细设计和实施提供更好的理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce98/9878488/18a9e1f32022/10639_2023_11604_Fig1_HTML.jpg

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