On the predictors of computational thinking and its growth at the high-school level
On the predictors of computational thinking and its growth at the high-school level
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DOI：
10.1016/j.compedu.2020.104060
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发表时间：
2021-02-01
影响因子：
12
通讯作者：
Guggemos, Josef
中科院分区：
文献类型：
--
作者：
Guggemos, Josef
计算思维 (CT) 是 21 世纪的一项关键技能。本文通过在纵向和自然的课堂环境中调查高中生的 CT 预测因素，为 CT 研究做出了贡献。假设的预测因素分为三个领域：学生特征、家庭环境和学习机会。 CT 通过计算思维测试 (CTt) 进行测量，这是一种既定的性能测试。以一学年三个时间点N=202名高中生为样本，采用潜在生长曲线建模作为分析方法。 CT 自我概念与 CT 水平的关联性最强，其次是推理能力和性别。计算机素养，其次是计算机使用时间和学年期间的正式学习机会，与 CT 增长的相关性最强。所有三个领域的变量似乎对于预测 CT 水平或生长都很重要。 CT 水平的解释方差为 70.4%，CT 增长的解释方差为 61.2%，这可能表明概念框架的全面性和简洁性之间存在良好的权衡。这些发现有助于更好地理解 CT 作为一种结构，并对 CT 教学产生影响，例如计算机科学的作用和 CT 学习中的动机。
Computational thinking (CT) is a key 21st-century skill. This paper contributes to CT research by investigating CT predictors among upper secondary students in a longitudinal and natural classroom setting. The hypothesized predictors are grouped into three areas: student characteristics, home environment, and learning opportunities. CT is measured with the Computational Thinking Test (CTt), an established performance test. N = 202 high-school students, at three time points over one school year, act as the sample and latent growth curve modeling as the analysis method. CT self-concept, followed by reasoning skills and gender, show the strongest association with the level of CT. Computer literacy, followed by duration of computer use and formal learning opportunities during the school year, have the strongest association with CT growth. Variables from all three areas seem to be important for predicting either CT level or growth. An explained variance of 70.4% for CT level and 61.2% for CT growth might indicate a good trade-off between the comprehensiveness and parsimony of the conceptual framework. The findings contribute to a better understanding of CT as a construct and have implications for CT instruction, e.g., the role of computer science and motivation in CT learning.