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Abstract：
In light of the increasing importance digital economy, the significance of computational thinking has grown exponentially, becoming imperative in both workplace and academic settings such as universities. This article addresses the critical need to comprehend the factors influencing the acceptance of computational thinking. The dataset introduces an extensive questionnaire comprising five constructs and 25 items, rooted in the extended Technology Acceptance Model. Notably, the model incorporates facilitating conditions and subjective norm, providing a comprehensive framework for understanding acceptance. Data collection involved 132 undergraduate university students sampled through purposive sampling, specifically targeting courses with a focus on computational thinking. The resulting dataset serves as a valuable resource for future research, offering detailed insights into the factors determining the acceptance of technology in educational contexts beyond mere thinking skills. Given the scarcity of research on technology acceptance in developing nations, this dataset holds particular significance, serving as a foundation for potential cross-cultural comparisons. The dataset contributes to the field by presenting a robust acceptance model, explaining 74.2 per cent of the variance in behavioural intention, 60.2 per cent in perceived usefulness, and 56.1 per cent in perceived ease of use. This high explanatory power positions the dataset as a superior resource for replication, benchmarking, and broader applicability in diverse contexts, thereby enhancing the understanding of computational thinking acceptance across different populations and settings. This dataset stands among the pioneering efforts to assess the novel covariance-based structural equation model algorithm within SmartPLS 4, presenting a valuable resource for future research employing the same mechanism.
摘要：
鉴于数字经济日益重要，计算思维的重要性呈指数级增长，在工作场所和大学等学术环境中变得势在必行。本文解决了理解影响计算思维接受的因素的迫切需要。该数据集介绍了一个广泛的问卷，包括五个结构和25个项目，植根于扩展的技术接受模型。值得注意的是,该模型结合了便利条件和主观规范，提供一个全面的框架来理解接受。数据收集涉及132名本科生，通过目的抽样进行抽样，专门针对以计算思维为重点的课程。由此产生的数据集作为未来研究的宝贵资源，提供详细的见解，以确定在教育环境中接受技术的因素，而不仅仅是思维技能。鉴于发展中国家对技术接受度的研究很少，这个数据集具有特殊的意义，作为潜在的跨文化比较的基础。数据集通过提供一个强大的接受模型来为该领域做出贡献，解释了74.2%的行为意向差异，60.2%的感知有用性，和56.1%的感知易用性。这种高解释能力将数据集定位为复制的优越资源，基准测试,以及在不同背景下更广泛的适用性，从而增强了对不同人群和环境中计算思维接受度的理解。该数据集是评估SmartPLS4中新颖的基于协方差的结构方程模型算法的开创性工作之一，为采用相同机制的未来研究提供了宝贵的资源。
