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Seow, P. S. K., Bimlesh Wadhwa, Lim, Z.-X., & Looi, C.-K. (2020). Towards using computational modeling in learning of physical computing: An observational study in Singapore schools. In S. C. Kong, H. U. Hoppe, T. C. Hsu, R. H. Huang, B. C. Kuo, K. Y. Li, C. K. Looi, M. Milrad, J. L. Shih, K. F. Sin, K. S. Song, M. Specht, F. Sullivan, & J. Vahrenhold (Eds.), Proceedings of International Conference on Computational Thinking Education 2020 (pp. 21-26). The Education University of Hong Kong.
Coding for students is no longer just constrained to software and screen-based text and graphics. Students today use programmable sensors and microprocessors to solve the problems around them. The purpose of this research is to understand how students conceptualize problems and implement solutions with physical computing. Our study is driven by the following: 1) find out what Computational Thinking (CT) competencies, specifically abstraction, decomposition and algorithmic thinking, can be developed by students and 2) to what level students develop these competencies in carrying out physical computing projects. We closely observe how 41 Grade 7 students developed solutions for problems they identify in the physical world around them. Through doing so, we explore how powerful ideas of CT play a role in a project-approach to physical computing. We believe open-ended exploration through a project-approach in physical computing should reinforce practices where CT skills can grow and flourish. Our findings show that much of students’ interaction with sensors and devices is at pre-CT level, where students simply use pre-existing code fragments or templates. As students gain skills and confidence, they can be explicitly guided to develop CT skills with new projects of their own design justifying their choices. We strongly believe that Computational Modeling (CM) could help students develop their CT skills e.g. abstraction, decomposition, and algorithmic approach much more than the minimally guided syntax driven teaching approaches.
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