连续性
元认知
心理学
辍学(神经网络)
数学教育
验证性因素分析
结构方程建模
在线学习
计算机科学
社会心理学
多媒体
认知
机器学习
神经科学
作者
Yu-Fen Tsai,Chien hung Lin,Jon‐Chao Hong,Kai‐Hsin Tai
标识
DOI:10.1016/j.compedu.2018.02.011
摘要
Developments in technology have made online teacher training applicable to MOOCs, but the validation of MOOCs presents some challenges, including the high dropout rate and low continuance intention to learn via MOOCs. The purpose of this study is to propose a unified model integrating metacognition and learning interest to investigate continuance intention to learn via MOOCs. Data of 126 respondents were collected and subjected to confirmatory factor analysis. Furthermore, the relationships were tested with structural equation modeling and the results revealed that metacognition was positively related to three levels of learning interest (i.e., liking, enjoyment, and engagement). The three levels of learning interest were positively related to continuance intention to use MOOCs. The findings imply that enhancing learners' metacognition can contribute to increased online learning interest and continuance to learn with MOOCs, thereby reinforcing the benefits of developing teacher training programs via MOOCs.
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