The 2027 National Survey of STEM Teacher Preparation Programs

STEM teachers play a key role in STEM workforce development efforts and help set the foundation for future scientific and technological advancements and innovations. In particular, teachers help students develop curiosity, creativity, perseverance, and resilience needed to solve problems and think critically. Although research has identified several important elements of effective STEM teacher preparation programs (TTPs), there is little research focused on the extent to which or how these elements are being enacted on a national scale. Additionally, carrying out this research requires access to a nationwide roster of STEM TPPs, which does not currently exist. The 2027 National Survey of STEM Teacher Preparation Programs (2027 NSSTPP), conducted by Horizon Research, Inc. (HRI) with assistance from Westat, will fill both gaps. The study complements the 2027 National Survey of Science, Mathematics, Computer Science, and Engineering Education (NSSME+). The NSSME+ provides extensive data about multiple facets of the STEM education system in the U.S., including STEM teacher preparedness, beliefs, and classroom practices, as well as, for newer teachers, experiences they had in their preparation to become a teacher. Conducting the 2027 NSSTPP in conjunction with NSSME+ provides an opportunity to examine how features of STEM preparation programs may influence teachers’ reported classroom practices and beliefs and provide future direction for program improvements. The 2027 NSSTPP develops foundational knowledge about how STEM teachers are being prepared in the U.S. The goals of the study are to understand (1) how often and in what ways STEM TPPs incorporate evidence-based practices in preparing teachers, and (2) key factors that influence enactment of those practices. Evidence-based practices of TTPs include providing strong content knowledge; pedagogical content knowledge; integration of STEM disciplines; extensive high quality clinical experiences and mentorship; and induction support. The proposed study will also seek initial evidence of the efficacy of an AI-assisted approach for compiling information about STEM TPPs from online sources that can be scaled up in future research efforts at lower cost. The study will use a stratified random sample that includes both traditional and alternative certification programs to provide nationally representative data about STEM TPPs. The size of the sample will allow the results to be disaggregated by key factors (e.g., traditional vs. non-traditional, undergraduate vs. graduate). Data collection instruments will be validated by expert review and through cognitive interviews to ensure questions are clear and interpreted as intended. Results will be disseminated through a variety of mechanisms and de-identified data will be made available for secondary analysis by other researchers. This project was funded by the STEM K-12 program, which supports fundamental, applied, and translational research on STEM learning in both formal K-12 education and informal learning environments 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.