摘要
本文基于绩效管理理论视角,探索性构建在线学习绩效监测预警体系框架,并且进行案例应用研究。研究结果表明,应该基于学习行为、学习满意度和学习结果三个维度内容要素进行在线学习绩效监测预警体系框架的设计构建;以非线性门限回归模型作为“信息处理中枢”,能够提高在线学习绩效监测预警体系框架的有效性;应用所构建的在线学习绩效监测预警体系框架可以实现对在线学习绩效的“绩效管理导向”式量化度量和全景展现,为在线学习资源配置与优化提供不同角度的决策支持科学依据。因此,需要以充分发挥在线学习环境的大数据资源优势,以及门限回归模型等非线性模型在大数据环境中的功效优势为双轮支撑,持续强化对在线学习绩效监测预警体系框架的理论研究与实践应用。
This article is based on the perspective of performance management theory,exploring the construction of an online learning performance monitoring and warning framework,and conducting case application research.The research results indicate that the design and construction of an online learning performance monitoring and warning framework should be based on three dimensions of content elements:learning behavior,learning satisfaction,and learning outcomes;Using a nonlinear threshold regression model as the“information processing hub”can improve the effectiveness of online learning performance monitoring and warning frameworks;The online learning performance monitoring and warning framework constructed by the application can achieve a“performance management oriented”quantitative measurement and panoramic display of online learning performance,providing scientific decision-making support from different perspectives for the allocation and optimization of online learning resources.Therefore,it is necessary to fully leverage the advantages of big data resources in online learning environments,as well as the efficacy advantages of nonlinear models such as threshold regression models in big data environments,as a dual round support,and continuously strengthen the theoretical research and practical application of online learning performance monitoring and warning frameworks.
作者
张峻赫
周启柏
宋娟
ZHANG Jun-he;ZHOU Qi-bai;SONG Juan
出处
《科学决策》
CSSCI
2024年第1期143-153,共11页
Scientific Decision Making
基金
北京市职业教育教学改革项目(项目编号:GB2022001)
北京市教育科学规划重点项目(项目编号:CAHA17098)
教育部人文社会科学研究专项任务项目(项目编号:22JDSZ3120)。
关键词
在线学习
绩效管理
监测预警
体系框架
online learning
performance management
monitoring and warning
system framework
