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Development and Predictive Validity of the Computational Thinking Disposition Questionnaire

Author

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  • Morris Siu-Yung Jong

    (Department of Curriculum and Instruction & Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong SAR, China)

  • Jie Geng

    (China Institute of Regulation Research, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Ching Sing Chai

    (Department of Curriculum and Instruction & Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong SAR, China)

  • Pei-Yi Lin

    (Department of Curriculum and Instruction & Centre for Learning Sciences and Technologies, The Chinese University of Hong Kong, Hong Kong SAR, China)

Abstract

Providing humans with quality education is regarded as one of the core pillars supporting the sustainable development of the world. The idea of computational thinking (CT) brings an innovative inspiration for people to adapt to our intelligent, changing society. It has been globally viewed as crucial that 21st-century learners should acquire the necessary skills to solve real-world problems effectively and efficiently. Recent studies have revealed that the nurture of CT should not only focus on thinking skills, but also on dispositions. Fostering students’ CT dispositions requires the cultivation of their confidence and persistence in dealing with complex problems. However, most of the existing measurement methods related to CT pivot on gauging thinking skills rather than dispositions. The framework of the CT disposition measurement model proposed in this paper was developed based on three theoretical features of thinking dispositions: Inclination, capability, and sensitivity. A two-phase analysis was conducted in this study. With the participation of 640 Grade 5 students in Hong Kong, a three-dimensional construct of the measurement model was extracted via exploratory factor analysis (16 items). The measurement model was further validated with another group of 904 Grade 5 students by confirmative factor analysis and structural equation modeling. The results align with the theoretical foundation of thinking dispositions. In addition, a CT knowledge test was introduced to explore the influences between students’ CT dispositions and their CT knowledge understanding.

Suggested Citation

  • Morris Siu-Yung Jong & Jie Geng & Ching Sing Chai & Pei-Yi Lin, 2020. "Development and Predictive Validity of the Computational Thinking Disposition Questionnaire," Sustainability, MDPI, vol. 12(11), pages 1-17, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4459-:d:365393
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    References listed on IDEAS

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    1. Belkis Díaz-Lauzurica & David Moreno-Salinas, 2019. "Computational Thinking and Robotics: A Teaching Experience in Compulsory Secondary Education with Students with High Degree of Apathy and Demotivation," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    2. Morris S.Y. Jong & Junjie Shang & Fong-Lok Lee & Jimmy H.M. Lee, 2008. "Harnessing Computer Games in Education," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 3(3), pages 54-61, July.
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    Cited by:

    1. Zhenzhen He & Xuemei Wu & Qiyun Wang & Changqin Huang, 2021. "Developing Eighth-Grade Students’ Computational Thinking with Critical Reflection," Sustainability, MDPI, vol. 13(20), pages 1-21, October.
    2. Hsi-Hung Peng & Astrid Tiara Murti & Lusia Maryani Silitonga & Ting-Ting Wu, 2023. "Effects of the Fundamental Concepts of Computational Thinking on Students’ Anxiety and Motivation toward K-12 English Writing," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    3. Miao Yue & Morris Siu-Yung Jong & Yun Dai, 2022. "Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review," Sustainability, MDPI, vol. 14(23), pages 1-29, November.

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