混合SEM-ANN模型用于预测本科生基于感知到的教育和情感支持的电子学习继续意图
Shanshan Xu1, Yangxin Wang1, Wenbo Luo2
1School of Fine Arts, Yuzhang Normal University, Nanchang, Jiangxi, China.
PloS one
|December 13, 2024
概括
大学的学生大学生.
科学领域:
- 教育技术的教育技术
- 心理学 心理学 心理学
- 社会学 社会学 社会学
背景情况:
- 在全球范围内,电子学习的采用正在增长.
- 了解影响学生参与度的因素至关重要.
- 预期确认模型 (ECM) 为研究用户行为提供了一个框架.
研究的目的:
- 调查教育和情感支持对大学生继续电子学习的意图的影响.
- 分析感知到的有用性,确认和对延续意向的满意度的预测能力.
- 将预期确认模型 (ECM) 应用于高等教育电子学习的背景.
主要方法:
- 在368名大学生中进行了调查研究.
- 结构方程建模 (SEM) 和人工神经网络 (ANN) 用于数据分析.
- 测量六个关键构造: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,满意度和继续意图.
主要成果:
- 满意度是持续意图的最强预测因素 (100%的正常化重要性).
- 确认 (29.9%) 和感知有用性 (28.3%) 也在很大程度上预测了继续工作的意图.
- 感知到的教育和情感支持直接影响了感知到的有用性,但通过其他因素间接影响了继续的意图.
结论:
- 满意度和确认是学生继续使用电子学习的关键驱动因素.
- 虽然直接支持的影响较小,但它的间接影响是显著的.
- 调查结果与联合国可持续发展目标4相一致,促进优质教育和终身学习.
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