中国教育类核心期刊 CSSCI来源期刊 RCCSE中国权威学术期刊

杂志文章

您的位置:首页 >文章目录 >2022年第2期>

    学习分析对自我调节学习的影响机理研究

    Study on Influence Mechanism of Learning Analytics on Self-regulated Learning

【作      者】:

徐晓青, 赵 蔚, 刘红霞, 姜 强, 李绿山, 程 诺


【关 键 词 】:

学习分析; 自我调节学习; 质性研究; 认知网络分析; 影响机理


【栏      目】:

课程与教学


【中文摘要】:

年来,众多学者将学习分析应用于自我调节学习以优化学习过程和学习效果,但当前学习分析的应用多以数据为驱动,尚未涉及学习者内在要素的变化规律,无法得知学习分析是如何影响自我调节学习的。为回答该问题,研究首先基于现有研究和理论归纳学习分析与自我调节学习的内在联系。其次,以学习分析的普遍应用模式为背景,收集学习者自我调节学习后的反思和访谈数据,利用认知网络分析和语义分析挖掘自我调节学习受学习分析影响的要素变化规律和各阶段触发特征。最后,总结变化规律和触发特征得到学习分析对自我调节学习的影响机理。结果表明,学习分析介入后,切实促进了自我调节学习各要素的融合,且在自我调节学习的不同阶段,学习分析触发自我调节学习发生的起点和路径存在差异。研究结论为促进学习分析更深入地支持自我调节学习提供了发展方向和理论依据。


【英文摘要】:

In recent years, many scholars have applied learning analytics to self-regulated learning to optimize the learning process and learning effect. However, most of the current applications of learning analytics are data-driven and have not yet addressed the change rules of learners' internal factors, so it is impossible to know how learning analytics affects self-regulated learning. In order to answer this question, this study firstly summarizes the intrinsic connections between learning analytics and self-regulated learning based on existing research and theories. Secondly, this study collects the reflective and interview data of learners after self-regulated learning in the context of the common application model of learning analytics, and uses cognitive network analysis and semantic analysis to explore the change rules of self-regulated learning factors affected by learning analytics and trigger characteristics at each stage. Finally, this study summarizes the changing rules and trigger characteristics to obtain the mechanism of the influence of learning analytics on self-regulated learning. The results show that the learning analytics effectively promote the integration of various factors of self-regulated learning, and the starting points and paths of self-regulated learning triggered by learning analytics are different at different stages of self-regulated learning. The results provide a direction and theoretical basis for promoting learning analytics to support self-regulated learning more deeply.

网站首页 | 联系我们 | 意见建议| 杂志订阅| 管理部登录

版权所有 © 电化教育研究 CopyRight ©e-EDUCATION RESEARCH 2011-2018 电话:0931-7971823 地址:西北师范大学《电化教育研究》杂志社