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Developing Computational Thinking in Primary School Students Through a Smart Farming Context Using the Engineering Design Process |
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| รหัสดีโอไอ | |
| Creator | Tussatrin Wannagatesiri |
| Title | Developing Computational Thinking in Primary School Students Through a Smart Farming Context Using the Engineering Design Process |
| Contributor | Supachai Kongpui |
| Publisher | Science Education Association (Thailand) |
| Publication Year | 2569 |
| Journal Title | International Journal of Science Education and Teaching (IJSET) |
| Journal Vol. | 5 |
| Journal No. | 2 |
| Page no. | 84-96 |
| Keyword | Computational thinking, Engineering Design Process, smart farming, primary education |
| URL Website | https://so07.tci-thaijo.org/index.php/IJSET/index |
| Website title | International Journal of Science Education and Teaching |
| ISSN | 2821-9163 |
| Abstract | The purpose of this research was to: (1) develop learning activities using the Engineering Design Process (EDP) within a smart farming context to enhance the computational thinking (CT) skills of primary school students,and (2) compare the students’ CT skills before and after participating in these activities. The participants were 39 fifth-grade students from a large inclusive primary school in Thailand, selected through purposive sampling. The research employed a pre-experimentaldesign. Research instruments included four EDP-based smart farming lesson plans totaling 12 hours of instruction,a CT concept test,and a performance-based CT rubric.The results indicated that the integration of the six-step EDP, ranging from identifying authentic agricultural problems to iteratively debugging automated sensor prototypes, significantly improved students’ CT skills. Quantitative analysis revealed a statistically significant increase in overall CT skill scores, with the post-test mean (M = 19.46, SD = 2.35) being significantly higher than the pre-test mean (M = 11.64, SD = 2.98) at p < .001. Significant improvements were observed across all specific computational components, with the largest improvement occurring in conditional logic. Furthermore, qualitative data from the performance-based rubric and final smart farming projects confirmed that students effectively applied the four pillars of CT:decomposition, pattern recognition, abstraction, and algorithm designto solve real-world agricultural challenges. This study concludesthat anchoring computational tasks in authentic, tangible contexts like smart farming successfully bridges the gap between abstract coding and meaningful physical outcomes. |
