在线学习准备如何预测在线学习情绪状态和预期学业成绩:检验一个基于理论的中介模型

Hsiang-yu Chien, Yu-Chen Yeh, Oi-man Kwok
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引用次数: 1

摘要

疫情期间,在线课程成为美国和世界各地大多数高等院校的主要授课形式。然而,许多学生因此经历了情绪困扰,并努力适应远程学习。为了探索情绪困扰与在线学习的其他方面(包括在线学习准备和学术成果)之间的关系,我们在2020年秋季学期邀请了80名大学生参加了一项在线调查。使用k-means聚类分析发现了两种不同的在线学习准备模式。有学习准备的在线学习者在焦虑、无聊和满意度上与没有准备好的在线学习者有统计学上的显著差异。此外,利用结构方程模型(SEM)检验了基于在线学习准备、情绪状态和学习结果期望理论关系的三路径中介模型。结果显示,准备程度正向预测满意度;此外,只有满意度能预测学习期望和期望成绩。讨论了这些发现的意义和本研究的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Online Learning Readiness Can Predict Online Learning Emotional States and Expected Academic Outcomes: Testing a Theoretically Based Mediation Model
During the pandemic, online courses became the major delivery format for most institutions of higher learning across the United States and around the world. However, many students experienced emotional distress as a result and have struggled to adapt to remote learning. To explore how emotional distress relates to other aspects of online learning, including online learning readiness and academic outcome, we asked a sample of 80 college students to participate in an online survey in the fall semester of 2020. Two distinct online learning readiness patterns were found using k-means cluster analysis. Online learning-ready learners showed statistically significant differences from the not-ready online learners on anxiety, boredom, and satisfaction. Moreover, a three-path mediation model based on a theoretical relationship between online learning readiness, emotional state, and expectation of learning outcome was tested using structural equation modeling (SEM). Results showed that readiness positively predicted satisfaction; furthermore, only satisfaction predicted learning expectation and expected grade. The implications of these findings and limitations of the study are discussed. 
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