Xu Shanshan, Wang Yangxin, Luo Wenbo
School of Fine Arts, Yuzhang Normal University, Nanchang, Jiangxi, China.
School of Fine Arts, Jiangxi Normal University, Nanchang, Jiangxi, China.
PLoS One. 2024 Dec 13;19(12):e0308630. doi: 10.1371/journal.pone.0308630. eCollection 2024.
Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university students' continuance intention to engage in e-learning. Researchers conducted a survey using structured questionnaires among 368 university students from three universities in Jiangxi Province. They measured their self-reported responses on six constructs: perceived educational support, perceived emotional support, perceived usefulness, confirmation, satisfaction, and continuance intention. The relationships between predictors and continuance intention, characterized by non-compensatory and non-linear dynamics, were analyzed using Structural Equation Modeling combined with Artificial Neural Networks. Apart from the direct effects of perceived educational and emotional support on perceived usefulness being non-significant, all other hypotheses were confirmed. Furthermore, according to the normalized importance derived from the multilayer perceptron analysis, satisfaction was identified as the most critical predictor (100%), followed by confirmation (29.9%), perceived usefulness (28.3%), perceived educational support (22.6%), and perceived emotional support (21.6%). These constructs explained 62.1% of the total variance in the students' continuance intention to engage in e-learning. This study utilized a two-stage analytical approach, enhancing the depth and accuracy of data processing and expanding the methodological scope of research in educational technology. The findings of this study contribute to the United Nations' Sustainable Development Goal 4, which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030. It provides direction for future research in different environmental and cultural contexts.
基于期望确认模型（ECM），本研究探讨了感知到的教育支持和情感支持对大学生继续参与电子学习意愿的影响。研究人员使用结构化问卷对江西省三所大学的368名大学生进行了调查。他们测量了学生在六个构念上的自评回答：感知到的教育支持、感知到的情感支持、感知到的有用性、确认、满意度和继续参与意愿。使用结构方程模型结合人工神经网络分析了预测因素与继续参与意愿之间以非补偿性和非线性动态为特征的关系。除了感知到的教育支持和情感支持对感知到的有用性的直接影响不显著外，所有其他假设均得到证实。此外，根据多层感知器分析得出的标准化重要性，满意度被确定为最关键的预测因素（100%），其次是确认（29.9%）、感知到的有用性（28.3%）、感知到的教育支持（22.6%）和感知到的情感支持（21.6%）。这些构念解释了学生继续参与电子学习意愿总方差的62.1%。本研究采用了两阶段分析方法，提高了数据处理的深度和准确性，拓展了教育技术研究的方法范围。本研究的结果有助于实现联合国可持续发展目标4，该目标旨在到2030年确保提供包容和公平的优质教育，并促进所有人的终身学习机会。它为不同环境和文化背景下的未来研究提供了方向。
