This project aims to serve the national need for high-quality science, technology, engineering, and math (STEM) teachers. To do so, it will look for connections between information from potential teacher candidates, such as test scores and STEM grades; their admittance to and enrollment in STEM teacher education programs; and their later retention and effectiveness as STEM teachers. There is intense interest in improving the quality of the U.S. STEM teacher workforce. However, surprisingly little is known about whether information in applications to teacher education programs predict or do not predict STEM teacher retention and effectiveness. This study is designed to generate empirical evidence about admissions into teacher education programs, the crucial first step to influencing the quality of the nation’s STEM teacher workforce. The specific research questions to be investigated include: 1. Are specific types of applicant information predictive of STEM teacher retention? 2. Is applicant information predictive of STEM teacher effectiveness? 3. Is applicant information differentially predictive of retention along the effectiveness distribution? 4. Is applicant information differentially predictive of retention and effectiveness in high-need educational agencies? Answers to these questions may reveal connections between applicant information and teacher outcomes. Such information could inform decisions about recruitment and admissions into STEM teacher education programs. As a result, the project has the potential to improve the nation’s overall strategy for ensuring a high-quality STEM teacher workforce.
This project is a collaboration between the Center for Analysis of Longitudinal Data in Education Research at the American Institutes for Research, Washington State’s Education Research and Data Center and five Washington State universities that prepare STEM teachers (Central Washington University, Pacific Lutheran University, University of Washington, Washington State University, and Western Washington University). This research study is made possible by statewide data about students and teachers in Washington that include: 1) data from all high-need educational agencies in the state; 2) college transcript data about STEM teacher candidates enrolled in public colleges and universities in the state; and 3) data about admissions processes and admissions from the five collaborating Universities. These sources of quantitative data will be combined to permit regression-based analyses of the attributes of college applicants that are correlated with STEM teacher effectiveness and retention. The project will also collect qualitative data from faculty at collaborating universities to better understand what faculty in STEM teacher education programs value in prospective teacher candidates and whether what they value is related to admission and enrollment in the participating universities. The project is informed by economical and socio-cultural theories. The economical theoretical basis places teacher education program operations within a public labor market in which applicant information is filtered by decision makers’ backgrounds, experiences, and social contexts. Given the breadth of potential relevance of this work, project results will be disseminated not only through academic journals, but also through national and state conferences, project websites, and a project conference with stakeholders from around the state. This Track 4: Noyce Research project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 STEM teachers to become STEM master teachers in high-need school districts. It also supports research on the persistence, retention, and effectiveness of K-12 STEM teachers in high-need school districts.
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.
