Rosli Mohd Shafie, Awalludin Muhammad Fairuz Nizam, Han Cheong Tau, Saleh Nor Shela, Noor Harrinni Md
School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Malaysia.
Faculty of Education, Universiti Teknologi MARA, Puncak Alam, Selangor, Malaysia.
Data Brief. 2024 Apr 23;54:110463. doi: 10.1016/j.dib.2024.110463. eCollection 2024 Jun.
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.
鉴于数字经济的重要性日益增加，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都变得至关重要。本文探讨了理解影响计算思维接受度的因素的迫切需求。该数据集引入了一份广泛的问卷，包括五个构念和25个项目，基于扩展的技术接受模型。值得注意的是，该模型纳入了促进条件和主观规范，为理解接受度提供了一个全面的框架。数据收集涉及通过目的抽样选取的132名本科大学生，具体针对侧重于计算思维的课程。所得数据集是未来研究的宝贵资源，提供了详细的见解，有助于深入了解在教育背景下决定技术接受度的因素，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匮乏，该数据集具有特殊意义，可作为潜在跨文化比较的基础。该数据集通过提出一个强大的接受模型，为该领域做出了贡献，该模型解释了行为意向中74.2%的方差、感知有用性中60.2%的方差以及感知易用性中56.1%的方差。这种高解释力使该数据集成为复制、基准测试和在不同背景下更广泛应用的优质资源，从而增进了对不同人群和环境中计算思维接受度的理解。该数据集是在SmartPLS 4中评估基于协方差的新型结构方程模型算法的开创性努力之一，为未来采用相同机制的研究提供了宝贵资源。
