ABSTRACT
Secondary students with disabilities (SWDs) require evidence-based practices that promote their academic success. However, secondary teachers may feel unprepared to support secondary SWDs, which may reduce their likelihood of implementing evidence-based practices. Therefore, the authors investigated the influence of several preservice and in-service support factors on general and special education teachers’ perceived knowledge of evidence-based methods and where to access information on effective methods for serving secondary SWDs in the United States. Their secondary analysis revealed that multiple factors are associated with higher levels of perceived knowledge, such as participation in teacher preparation programs with an adequate focus on SWDs, regardless of teacher type. However, other factors, such as special education certification, were not related to teachers’ perceived knowledge. Given the influence of perceived knowledge on teacher implementation, these findings have important implications for researchers, teacher educators, and school and district administrators.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Alexandra Shelton
Alexandra Shelton is an Assistant Professor of Special Education at Johns Hopkins University. Her research interests include improving literacy outcomes for historically marginalized adolescents with disabilities and reading difficulties via evidence-based literacy instruction and intervention and teacher professional development and coaching. As a former high school special education teacher, Alexandra served students in the general and special education settings in English language arts, reading, math, and science.
Brennan Register
Brennan Register is a PhD student in the Quantitative Methodology: Measurement and Statistics program at the University of Maryland College Park. With a solid foundation in statistical analysis, she joined the University of Maryland following the successful completion of her Master’s in Statistics from the University of Pittsburgh. Brennan’s research lies in the application of cutting-edge statistical methodologies to complex educational data. She is particularly interested in investigating the performance of multilevel and standard prediction algorithms on large-scale educational datasets and strives to make a meaningful impact in the realm of data-driven decision-making for education.