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    面向计算思维发展的深度学习模型建构 ——以可视化编程教学为例

    Deep Learning Model Construction for the Development of Computational Thinking: Taking Visual Programming Teaching as An Example

    [浏览次数:20140]

【作      者】:

陈兴冶, 马颖莹, 杨 伊


【关 键 词 】:

深度学习; 计算思维; 可视化编程; 教学模式


【栏      目】:

课程与教学


【中文摘要】:

信息时代呼唤深度学习,它与计算思维存在着深刻且本质的关联。在信息技术学科教学中,如何发展学生计算思维是诸多中小学教师面临的教学困境。在此背景下,本研究建构了“面向计算思维发展的深度学习模型”,以知识建构、混合式学习理论和CTCL研究范式为指导,将课堂教学活动分为教师行为、学生行为、思维过程和评价总结四个方面,基于上述模型进行教学实验。实验以可视化编程课程为载体,历时18周,通过对三阶段两维度作品的评价,比较分析实验组和对照组学生的计算思维发展状况。结果表明:三个阶段实验组学生的计算思维能力优于对照组学生,计算思维能力提升水平高于对照组,实验组和对照组的计算思维各维度的发展水平比较均衡。由此可得出结论,该学习模型可以有效提升学生的计算思维水平并促进深度学习的发生。


【英文摘要】:

The information age calls for deep learning, which is deeply and intrinsically related to computational thinking. In the teaching of information technology, how to develop students' computational thinking is a teaching dilemma faced by many primary and secondary school teachers. In this context, this study constructs a "deep learning model for the development of computational thinking". Guided by knowledge construction, blended learning theory and CTCL research paradigm, classroom teaching activities are divided into four aspects: teacher behavior, student behavior, thinking process and evaluation summary. Based on this model, an eighteen-week experiment is conducted in a visual programming course. Through the evaluation of the works of three stages and two dimensions, the development of computational thinking of students in experimental group and control group is compared and analyzed. The results indicate that in the three stages, the computational thinking ability of the experimental group is better than that of the control group, and the improvement level of computational thinking ability is higher than that of the control group. The development level of each dimension of computational thinking in the experimental group and the control group is relatively balanced. It can be concluded that this learning model can effectively improve students' computational thinking level and promote the occurrence of deep learning.

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