AI 中文总结
研究旨在开发测量高等教育学生计算思维自我效能感的工具，通过创建候选项目、专家评估、收集学生数据进行主成分分析和验证性因素分析，最终形成可用于评估干预效果及促进CT技能发展的工具。
AI 中文摘要
许多努力致力于帮助不同教育水平的学生培养计算思维（CT）技能。自我效能感对技能发展很重要，它能预测在教育任务中的毅力、参与度和成功。我们创建了一个工具来测量高等教育学生在CT技能抽象、算法思维、分解、评估和概括方面的自我效能感。首先通过纳入、改编和扩展文献中的项目创建了91个候选项目，经CT和教育领域专家评估后保留54项。为进一步减少项目数量，从270名高等教育学生中收集数据，通过主成分分析将项目数减至27项。验证性因素分析表明项目可分为创建解决方案和评估解决方案两类。该工具在评估高等教育学生CT自我效能感时可能有价值，经进一步验证可用于评估干预效果或决定提供何种干预以促进CT技能发展。
英文摘要
Much effort is put into helping students at different educational levels develop Computational Thinking (CT) skills. Self-efficacy is important for skill development. It can predict perseverance, engagement and success on educational tasks. We created an instrument to measure self-efficacy of students in higher education for the CT skills abstraction, algorithmic thinking, decomposition, evaluation and generalization. First, 91 candidate items were created by including, adapting and extending items found in the literature. These items were evaluated by experts in the field of CT and education. 54 items remained and to reduce the number of items further, data was collected from 270 students in higher education recruited both through Prolific and a university setting in Costa Rica. Through principle component analysis (PCA) using a subset of 200 responses, the number of items was reduced to 27. Confirmatory factor analysis (CFA) using the remaining responses in the dataset showed the items can be divided into two categories: (1) creating the solution and (2) evaluating the solution. The created instrument can be valuable when assessing CT self-efficacy of students in higher education. With additional validation (e.g. examination of test-retest validity), we believe the scale could be used to evaluate the effectiveness of interventions, or decide what interventions should be provided to foster CT skill development.
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