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    大数据的知识发现:促进课堂深度学习的策略

    Knowledge Discovery Based on Big-Data:Strategies to Promote Deep Learning in the Classroom

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【作      者】:

马如霞, 王 陆, 彭 玏


【关 键 词 】:

深度学习; 布鲁姆认知目标分类; 课堂对话分析; 课堂教学行为大数据


【栏      目】:

课程与教学


【中文摘要】:

深度学习的理论只有应用于课堂实践,才能真正促进教育教学的变革。为了探究课堂深度学习的现状以及促进课堂深度学习的策略,文章基于布鲁姆认知目标分类理论,对174节中小学课堂师生对话采用统计分析方法、滞后序列分析法、社会网络分析方法、数据挖掘和知识发现方法,研究发现并得出如下结论:第一,当前课堂教学呈现浅层教学的样态,课堂教学存在理解层级的固化等问题;第二,增加批判性问题,提升反思指导,是促进深度学习有效发生的教学策略;第三,弥补评判和执行等关键教学行为,将其作为知识建构过程中的中介和桥梁,构建层进式学习;第四,转变教育信念,增强与创造认知层级中生成、计划和产生有关的创生性教学活动,开展生成性教学。


【英文摘要】:

Only when deep learning theory is applied to classroom practice, can it truly promote the improvement of education and teaching. In order to explore the current situation of deep learning in the classroom and the strategies to promote deep learning in the classroom, this paper analyses 174 sections of teacher-student dialogues in primary and secondary schools based on Bloom's cognitive taxonomy, using statistical analysis, lag sequence analysis, social network analysis, data mining and knowledge discovery methods. This study finds and concludes the following: firstly, the current classroom teaching presents a low cognitive level teaching pattern and classroom teaching has problems such as the solidification of understanding levels. Secondly, increasing critical questions and enhancing reflective instruction are teaching strategies that promote the effective occurrence of deep learning. Thirdly, key teaching behaviors such as judging and performing are mediators and bridges in the process of knowledge construction, which builds cascading learning. And fourthly, teachers should transform their educational beliefs to enhance generative teaching activities related to generating, planning and producing in the cognitive hierarchy of creativity.

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