Decoding Urban Ecosystems: Computational Thinking Integration in Middle School STEM

American Museum Natural History, New York NY

Investigators

Abstract

As a result of the powerful innovation and application of computing in STEM disciplines, the STEM+C program addresses an urgent need for real-world, interdisciplinary, and computational preparation of students from the early grades through high school (preK-12). The American Museum of Natural History (AMNH), in collaboration with Project GUTS (Growing Up Thinking Scientifically), will undertake a three-year research project to design, implement, and evaluate a youth-centered program that engages New York City (NYC) middle school students in a new computational thinking (CT)-integrated science curriculum. In this curriculum, students will simultaneously engage with ecosystem dynamics and use CT practices in the context of computer modeling and simulations, including abstraction, automation (including algorithmic thinking, debugging, and iteration) analysis, and generalization through computer modeling and simulation. The project research aims to deepen understanding of student learning and active engagement with authentic science practices when students are given supports to make connections between CT in coding and decoding of computer models and scientific processes. In addition, the curriculum design will explicitly connect STEM content to the professional practices of the field, thereby contributing to existing literature on effective approaches. Through this project, 125 students will participate in a new, CT-integrated ecosystems curriculum. These participants will be recruited from schools that predominantly serve low-income families who are representative of the socioeconomic, ethnic, and gender diversity of NYC. The project will also engage four NYC teachers as co-researchers. Data to be collected include modified versions of existing knowledge assessment and survey instruments, artifact-based interviews conducted with students, and recorded observations of instruction. Pre-post analysis will be conducted to identify learning gains, and a modified interaction analysis within and between data sources will be conducted to illuminate relationships between students' analysis of computational models and development of mechanistic understandings of ecosystems. Findings will be broadly disseminated and will inform the design of a professional learning program for NYC middle school teachers. The tested curriculum will be available on Project GUTS' open professional development network. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.