Development and Predictive Validity of the Computational Thinking Disposition Questionnaire

Morris Siu-Yung Jong, Jie Geng, Ching Sing Chai and Pei-Yi Lin
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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

Sustainability, 2020, vol. 12, issue 11, 1-17

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.

Keywords: computational thinking disposition; cognitive measurement; coding education (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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