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NSFDirectorate for STEM Education

Collaborative Research: Designing a Professional Learning Model to Develop Teachers Professional Vision for Integrating Computational Thinking in K-12 Science Courses

Active
Institution
University of California-Berkeley, CA
Principal investigator
Eric J Greenwald
Funding
$1.3M
Duration
2025–2029
Open portfolio

Moonbase score

Still being scored

Abstract

The Lawrence Hall of Science at UC Berkeley and the American Institutes for Research propose a transformative 4-year study to build teacher capacity for integrating Computational Thinking (CT) into Grades 6–8 science classrooms. This initiative will advance current priorities of the NSF ITEST and DRK-12 programs by addressing the critical gap in computational practices within science curricula through the creation and implementation of a scalable professional learning model for middle school science teachers. The project will support teacher professional vision and adaptive expertise for CT-infused instruction through instructional coaching, professional development workshops, and an online professional learning community. These efforts aim to empower teachers to design, enact, and adapt CT-integrated lessons that foster students’ positive attitudes toward science and enhance their knowledge of science and CT. The project will directly serve over 1,000 students and create a teacher professional learning model that is positioned for scale to serve teachers and students across the country. The project builds on prior research and frameworks developed by the project team, ensuring the integration of evidence-based strategies and a content focus on authentic computational and scientific practices. In collaboration with multiple school districts, this study will pilot, refine, and implement the professional learning model, leveraging mixed-methods research to evaluate its impact. Data collection methods include classroom observations, teacher surveys, documentation of coaching sessions, and student assessments, all aimed at measuring changes in teacher practice and student outcomes. The project will develop a professional learning workshop and ongoing instructional coaching for in-service science teachers to support them in integrating CT skills and concepts into their science curriculum and instruction. Analysis of project data will provide evidence of the relationship between program participation and teacher and student outcomes, supporting future implementation of the professional learning model. By advancing knowledge on how CT integration can transform science education, the project aspires to broaden participation in STEM fields among all students. The dissemination of findings through conferences, policy briefs, and practitioner resources ensures scalability and sustainability, enabling educators nationwide to adapt existing science curricula to incorporate computational thinking effectively. The Discovery Research preK-12 program (DRK-12) is an applied research program that seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for funded projects. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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.

Research team

  • Eric J GreenwaldPI
  • Ari KrakowskiCo-PI

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Directorate for STEM EducationDiscovery Research K-12STEM Learning & Learning Environmentsteachersteachercomputational thinkingprofessional learningteacher professionaldrk-learning modelcoaching

Questions for University of California-Berkeley

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