Abstract Computational thinking (CT) is a key 21st-century skill. This paper contributes to CT research by providing a comprehensive picture of CT predictors in a longitudinal and natural classroom setting among upper secondary students. 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. Overall, 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. Our findings contribute to a better understanding of CT as a construct and have implications for instruction, e.g., the role of computer science and motivation in CT learning.

中文翻译：

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计算思维的预测因素及其在高中阶段的发展

摘要 计算思维 (CT) 是 21 世纪的一项关键技能。本文通过在高中生的纵向和自然课堂环境中提供 CT 预测因子的全面图片，为 CT 研究做出贡献。假设的预测因素分为三个方面：学生特征、家庭环境和学习机会。CT 是通过计算思维测试 (CTt) 来衡量的，这是一种既定的性能测试。N = 202名高中生，在一学年的三个时间点作为样本，潜在增长曲线建模作为分析方法。CT自我概念，其次是推理能力和性别，与CT水平的关联最强。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。总体而言，来自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 增长为 61.2%，这可能表明概念框架的全面性和简约性之间存在良好的权衡。我们的发现有助于更好地理解 CT 作为一种结构，并对教学产生影响，例如，计算机科学和动机在 CT 学习中的作用。