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Board 69: Integrating Computational Thinking, Engineering Design, and Environmental Science Through Smart Greenhouses

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

NSF Grantees Poster Session

Tagged Topics

Diversity and NSF Grantees Poster Session

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Paper Authors


David W. Jackson Boston College, Lasell College, and Waltham (MA) Public Schools Orcid 16x16

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David W. Jackson is a PhD student in Curriculum & Instruction at Boston College, an Adjunct Professor with Lasell College, and an After-School STEM Coordinator with Waltham (MA) Public Schools.

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Helen Zhang Boston College

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Helen Zhang is a senior research associate working at the Lynch School of Education, Boston College. Her research interest includes science education, design thinking, and learning from failure.

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Mike Barnett Boston College

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Integrating Computational Thinking, Engineering Design, and Environmental Science through Smart Greenhouses

In this NSF DRL funded project we are supporting students to design tools (i.e., smart greenhouses with the Internet of Things and microelectronics) to carry out their self-driven scientific investigations around environmental science. Computing is transforming the fields of science, technology, engineering, and mathematics (STEM), including engineering design and scientific research. Computational thinking (CT) is now recognized as a foundational competency for meeting the demand for educated and technologically literate labor forces of future societies [1], [2], [3]. Yet the strategies for embedding computational thinking in STEM education – particularly in engineering education – are relatively underexplored. This project will contribute to the strategies gap by supporting students to integrate computational thinking with engineering design to build smart greenhouses for their own scientific investigations. Students will learn knowledge and practices in engineering and CT domains, and, more importantly, how engineering design with computing technologies can boost scientific research. In a pilot study for this project, we developed a 12-day curriculum on smart-greenhouse design and implemented it with two, 8th-grade science classrooms in a public school of an urban-ring city in Massachusetts. The 199 students worked in pairs and trios. First, they learned computational skills to control different devices (e.g., humidity sensors, temperature sensors, light sensors, LED strips, motors, and relays). Then, they used the devices to collect environmental data. Finally, they used engineering practices [4] to design their own greenhouses, by selecting and installing the devices and microelectronics to answer their scientific research questions. The research question of this efficacy study is, How and in what ways does the smart-greenhouse design project engage students in practices of computation, engineering, and science? We collected data on students’ interviews before and after the project, their design artifacts, and classroom observations of eight student pairs to explore student engagement during the smart greenhouse project. Preliminary data analysis compared students’ pre- and post-interview data. Subsequent data analysis leveraged intensity sampling [5] to generate data from video [6] for two groups that exemplified trends related to behavioral, cognitive, and affective engagement [7], as well as various shifts (or lacks thereof) in practices of computation, engineering, and science. Preliminary findings indicate that students generally broadened their conceptions of computing, as well as its connections with engineering and science. Students were affectively engaged by the personal relevance of the project (e.g., they could choose which plant/s to grow, and then later consume; they could work in teams on a final project, which they preferred to individual work on a final exam). The affective engagement supported behavioral and cognitive engagement, as excitement and camaraderie mitigated occasional frustration or disappointment. These findings provide valuable insights on integrating computational thinking in mainstream science classes, and will inform the design of future teacher workshops to better engage students in integrated learning of automated computing, environmental science, and engineering design.

Works Cited

[1] S. Grover and R. Pea, “Computational Thinking in K-12: A Review of the State of the Field,” Educational Researcher, vol. 42, pp. 38–43, 2013. [2] U.S. Department of Labor, “The STEM Workforce Challenge: The Role of the Public Workforce System in a National Solution for a Competitive Science, Technology, Engineering, and Mathematics (STEM) Workforce.” 2007. [3] J. M. Wing, “Computational thinking,” Communications of the ACM, vol. 49, pp. 33-35, 2006. [4] NGSS Lead States, Next Generation Science Standards: For states, by states. Washington, DC: The National Academies Press, 2013. [5] J. W. Creswell, Qualitative inquiry and research design: Choosing among five approaches, 3rd ed. Los Angeles, CA: Sage Publications, 2013. [6] F. Erickson, “Definition and analysis of data from videotape: Some research procedures and their rationales,” in Handbook of Complementary Methods in Education Research, J. L. Green, G. Camilli, P. B. Elmore, A. Skukauskaite, and E. Grace, Eds. Mahwah, NJ: Lawrence Erlbaum Associates, 2006, pp. 177-191. [7] J. A. Fredricks, P. C. Blumenfeld, and A. H. Paris, “School Engagement: Potential of the Concept, State of the Evidence,” Review of Educational Research, vol. 74, pp. 59–109, 2004.

Jackson, D. W., & Zhang, H., & Barnett, M. (2019, June), Board 69: Integrating Computational Thinking, Engineering Design, and Environmental Science Through Smart Greenhouses Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32406

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