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首页> 外文期刊>Interactive technology and smart education >Predicting the determinants of online learning adoption during the COVID-19 outbreak: a two-staged hybrid SEM-neural network approach
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Predicting the determinants of online learning adoption during the COVID-19 outbreak: a two-staged hybrid SEM-neural network approach

机译：在Covid-19爆发期间预测在线学习采用的决定因素：双分阶段的混合神经网络方法

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Purpose - This study aims to study the adoption of online learning in higher education through the perspective of the readiness of the following factors: self-directed learning (SDL), motivation for learning (ML), online communication self-efficacy (OCE) and learner control (LC). This was an empirical study in the context of developing countries, specifically Thailand. Design/methodology/approach - This research applied a quantitative study method by collecting data from 605 higher education students in autonomous government institutions. The data analysis applied a structural equation model (SEM) to identify the significant determinants that affected the adoption of online learning. Moreover, this study applied a neural network model to examine the findings from the SEM. Findings - From the data analysis using the SEM and neural network model, the results matched each other. The results of the empirical study were firm and supported that the readiness factors of students had statistical significance in the following order SDL, OCE, LC and ML. Practical implications - The study results showed an operational perspective to be prepared for online teaching, both for the related department of the Ministry of Education to support the infrastructure for online learning and for universities and instructors to create learning conditions and design teaching processes consistently with the online learning context. Originality/value - Since the learning management in the 21st century is focused on student-centred learning, the empirical results obtained from this study presented the view of learners' readiness that would influence the acceptance of online learning. In addition, this research presented the challenges and opportunities of online instruction during the COVID-19 pandemic.
机译：目的 - 本研究旨在通过以下因素的准备方式研究在高等教育中通过在线学习：自我指导学习（SDL），学习动机（ML），在线通信自我效能（OCE）和学习者控制（LC）。这是发展中国家，特别是泰国的实证研究。设计/方法/方法 - 本研究通过从605名高等教育学生中的自治政府机构收集数据来应用定量研究方法。数据分析应用了结构方程模型（SEM）来确定影响在线学习的重要决定因素。此外，该研究应用了神经网络模型来检查来自SEM的结果。调查结果 - 从使用SEM和神经网络模型的数据分析，结果彼此匹配。实证研究的结果是坚定的，并支持学生的准备因素在以下命令SDL，OCE，LC和ML中具有统计学意义。实际意义 - 研究结果表明，为在线教学中准备的在线教学，支持在线学习和大学和教师的基础设施，以创造学习条件和设计教学过程的基础设施在线学习背景。原创性/价值 - 由于21世纪的学习管理专注于以学生为中心的学习，从本研究中获得的实证结果提出了学习者的准备，这将影响在线学习的接受。此外，本研究介绍了Covid-19流行期间在线教学的挑战和机遇。

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