Panel Paper: The Coherence between Special Education Teachers’ Preparation and Early Career Experiences and Implications for Special Education Teacher Attrition

Saturday, November 9, 2019
Plaza Building: Concourse Level, Governor's Square 14 (Sheraton Denver Downtown)

*Names in bold indicate Presenter

Roddy Theobald1, Dan Goldhaber2, Valerie Lynch3, Darcy Miller4, Natsumi Naito2, Bill Rasplica5 and Marcy Stein6, (1)American Institutes for Research, (2)University of Washington, (3)Puget Sound Educational Service District, (4)Washington State University, (5)Franklin Pierce Schools, (6)University of Washington, Tacoma

Although a large body of empirical research demonstrates that teachers are the most important schooling factor in predicting a student’s academic success (e.g., Rivkin et al., 2005), very little large-scale empirical attention has been paid specifically to special education teachers. For example, despite longstanding concerns nationwide about the disproportionate attrition of special education teachers from the teaching profession and its contribution to the nationwide shortage of special education teachers (e.g., Brunsting et al., 2014), there is little large-scale empirical evidence about specific factors that make it more likely for special education teachers to stay in the profession. That said, an emerging literature on preservice teacher education (not specific to special education teachers) suggests that student teaching experiences (e.g., Ronfeldt, 2014) and the “match” between a candidate’s teacher education experiences and early-career experiences (e.g., Goldhaber et al., 2017) can have important implications for teachers’ career paths.

In this paper, we extend this emerging literature to focus specifically on the preservice experiences that predict retention for special education teachers. Specifically, we leverage a unique longitudinal data set from Washington State that combines data about preservice teacher candidate experiences with information about K–12 teacher and student outcomes. This data set has been assembled as part of the Teacher Education Learning Collaborative (TELC), a partnership with 15 teacher education programs (TEPs) in Washington designed to explore the effects of teacher education experiences on inservice teacher and student outcomes. The TELC data set is unique because it includes comprehensive student teaching data (such as where teacher candidates did their student teaching and which inservice teachers supervised it) for candidates from one of the TEPs participating in TELC and allows us to track these candidates into the state’s K–12 public school workforce. We combine the TELC data—that currently includes data on 2,209 special education teacher candidates, of whom 1,961 (89%) are observed as public school teachers in Washington—with novel survey data of special education faculty from participating TEPs and special education directors from school districts to investigate predictors of retention for these candidates.

While we plan to investigate a wide range of predictors from the TELC dataset, including characteristics of the candidate’s student teaching school and mentor teacher, we are particularly interested in the alignment or “coherence” (Grossman et al., 2008) between a candidate’s teacher preparation and early career experiences. The surveys of special education teacher preparation faculty ask about coverage of different instructional approaches, content, methods of instructional delivery, specific behavior programs and eligibility assessments in the programs’ coursework and field experiences, while the parallel surveys of district special education directors ask about current practices and expectations in their district’s special education program. We will use these surveys to construct measures of the extent to which individual teacher candidates experience coherence between the skills and content taught in their teacher preparation program and the expectations and practices of the districts in which they begin their teaching careers, and use these measures to predict their retention once they enter the teaching workforce.