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 , , . 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  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  to generate data from video  for two groups that exemplified trends related to behavioral, cognitive, and affective engagement , 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.
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