连续性
心理学
背景(考古学)
结构方程建模
心理干预
计算机科学
2019年冠状病毒病(COVID-19)
应用心理学
社会心理学
医学
生物
精神科
机器学习
病理
古生物学
传染病(医学专业)
疾病
作者
Xuan Wang,Tingting Liu,Jixin Wang,Jun Tian
标识
DOI:10.1080/10447318.2021.1938389
摘要
In response to the COVID-19 pandemic, the emergency policy for online learning at home has been launched in China. Live video, prerecorded video, and their combination are the three main video learning modes. Parental involvement emerges as a unique factor that may influence online learning effects. Students' behaviors such as interaction, engagement may vary from traditional online learning context due to multiple reasons. Hence, the previous acceptance models are not applicable. In this paper, by modifying and extending the Expectation Confirmation Model, a unified acceptance and continuance intention model (UACIM) is proposed to explore the factors affecting students online learning continuance intention. Confirmation is omitted for the lack of a priori knowledge. Perceived ease of use is included as the factor assessing technology performance. Parental involvement, interaction, and students' engagement are considered as external variables. The hypotheses are validated by the data collected from 306,139 students from Grade 4 to Grade 9 in Hubei Province, China. The results of structural equation modeling reveal that all variables are significant in explaining continuance intention, which indicates the vital roles of the external factors. Some negative correlations are found with suggestions that practitioners should be prudent when implementing interactions as well as offering individualized instructional designs. Furthermore, three video learning modes are found to be successful. The most preferable way is the hybrid mode with the most interactions, engagement, strongest satisfaction, perceived usefulness, and continuance intention. There are significant differences among three groups for most paths. Therefore, appropriate interventions can be devised to enhance learning effects and continuance intention when utilizing different video learning.
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