Unlocking insights: A comprehensive dataset analysis on the acceptance of computational thinking skills among undergraduate university students through the lens of extended technology acceptance model, HTMT, covariance-based SEM, and SmartPLS

• Published in: Data in Brief
• Main Author: Rosli M.S.; Awalludin M.F.N.; Han C.T.; Saleh N.S.; Noor H.M.
• Format: Data paper
• Language: English
• Published: Elsevier Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192140057&doi=10.1016%2fj.dib.2024.110463&partnerID=40&md5=96e58fb40e2ecceb010132e216771ad4

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. © 2024 The Authors