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Structural Validation for the Developmental Model of Computational Thinking
Journal of Educational Computing Research ( IF 4.345 ) Pub Date : 2021-06-02 , DOI: 10.1177/07356331211017794
Meng-Jung Tsai, Jyh-Chong Liang, Silvia Wen-Yu Lee, Chung-Yuan Hsu

A prior study developed the Computational Thinking Scale (CTS) for assessing individuals’ computational thinking dispositions in five dimensions: decomposition, abstraction, algorithmic thinking, evaluation, and generalization. This study proposed the Developmental Model of Computational Thinking through validating the structural relationships among the five factors of the CTS. To examine the model, a questionnaire including the CTS was administered to 472 middle school students. A confirmatory factor analysis was used to confirm the construct of the measurements, and a PLS-SEM analysis was used to validate the structural relationships among the factors. The results confirmed that the 19-item CTS has good item reliability, internal consistency, and construct reliability for measuring computational thinking (CT). In the Developmental Model of CT, decomposition and abstraction significantly predict all other three CT dispositions, suggesting that they are the two fundamental factors required for CT development. Moreover, a significant linear prediction path was shown starting from algorithmic thinking, evaluation, until generalization. Thus, a multi-level model was confirmed for the conceptual framework of CT. This model suggests a possible sequence for CT development which may provide a guideline for the teaching objectives of CT for different learning stages in different school levels. Decomposition and abstraction are especially suggested to be emphasized in school curricula before teaching algorithmic thinking or algorithm designs.



中文翻译:

计算思维发展模型的结构验证

先前的一项研究开发了计算思维量表 (CTS),用于从五个维度评估个人的计算思维倾向:分解、抽象、算法思维、评估和概括。本研究通过验证CTS五个因素之间的结构关系,提出了计算思维发展模型。为了检验该模型,对 472 名中学生进行了包括 CTS 的问卷调查。验证性因素分析用于确认测量的构建,PLS-SEM 分析用于验证因素之间的结构关系。结果证实,19项CTS在测量计算思维(CT)方面具有良好的项目信度、内部一致性和结构信度。在CT发展模型中,分解和抽象显着预测所有其他三种 CT 倾向,表明它们是 CT 发展所需的两个基本因素。此外,从算法思维、评估到泛化,显示了一条重要的线性预测路径。因此,CT的概念框架确定了一个多层次的模型。该模型提出了 CT 发展的可能顺序,可为不同学校不同学习阶段的 CT 教学目标提供指导。特别建议在教授算法思维或算法设计之前,在学校课程中强调分解和抽象。表明它们是 CT 发展所需的两个基本因素。此外,从算法思维、评估到泛化,显示了一条重要的线性预测路径。因此,CT的概念框架确定了一个多层次的模型。该模型提出了 CT 发展的可能顺序,可为不同学校不同学习阶段的 CT 教学目标提供指导。特别建议在教授算法思维或算法设计之前,在学校课程中强调分解和抽象。表明它们是 CT 发展所需的两个基本因素。此外,从算法思维、评估到泛化,显示了一条重要的线性预测路径。因此,CT的概念框架确定了一个多层次的模型。该模型提出了 CT 发展的可能顺序,可为不同学校不同学习阶段的 CT 教学目标提供指导。特别建议在教授算法思维或算法设计之前,在学校课程中强调分解和抽象。该模型提出了 CT 发展的可能顺序,可为不同学校不同学习阶段的 CT 教学目标提供指导。特别建议在教授算法思维或算法设计之前,在学校课程中强调分解和抽象。该模型提出了 CT 发展的可能顺序,可为不同学校不同学习阶段的 CT 教学目标提供指导。特别建议在教授算法思维或算法设计之前,在学校课程中强调分解和抽象。

更新日期:2021-06-03
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