【作 者】：
黄昌勤， 涂雅欣， 俞建慧， 蒋 凡， 李明喜
【关 键 词 】：
数据驱动； 在线学习； 学习倦怠； 学习预警； 智能教育
【栏 目】：
网络教育
【中文摘要】：
在线学习倦怠是学习者由于学习压力等因素影响而产生的一种倾向于逃避学习的消极心理状态，对其进行有效识别与适时预警是实现高效在线学习的重要途径。鉴于此，文章首先确立了在线学习倦怠的内涵与结构维度，并基于学习倦怠量化表征依据分析构建了数据驱动的在线学习倦怠预警模型；然后从在线学习倦怠预警过程出发，详细阐述了数据驱动在线学习倦怠预警的实现方案；最终依托iStudy学习平台完成了在线学习倦怠预警系统功能的设计与开发，并以H大学在线学习者为研究对象进行系统应用和实证分析。实践效果表明，该预警模型可以有效降低学习者倦怠水平并显著提升课程学习效果，为在线教育中的学习倦怠评估与智能化预警奠定了一定的基础。
【英文摘要】：
Online learning burnout is a kind of negative mental state that learners tend to avoid learning due to the increase of perceptual learning pressure, and effective identification and timely warning of burnout are important for efficient online learning. This paper first defines the connotation and dimensionality of online learning burnout, and constructs the data-driven online learning burnout early warning model based on the analysis of quantitative representation of learning burnout. Then, starting with the early warning process of online learning burnout, the implementation scheme of the data-driven early warning for online learning burnout is elaborated. Finally, relying on the iStudy learning platform, this study achieves the functional design and development of the online learning burnout early warning system, and the system is applied and empirically analyzed with online learners of H University as the research object. The results show the proposed model can effectively alleviate learners' burnout and significantly improve the learning effect, laying some foundation for learning burnout assessment and intelligent early warning in online education.
【下 载】：
数据驱动的在线学习倦怠预警模型研究与实现