Date of Conferral
8-13-2025
Degree
Doctor of Education (Ed.D.)
School
Education
Advisor
Michael Vinella
Abstract
The problem that was addressed in this study was that high school teachers in a Texas school district struggle to use data-driven decision making (DDDM) to meet the requirements of Texas HB 1416, which mandates specific interventions to address instructional loss. Grounded in the theory of DDDM, by Mandinach et al., the purpose of this basic qualitative study was to explore high school teachers’ experiences using DDDM to implement supplemental accelerated instruction (SAI) for students who failed to demonstrate proficiency on state exams. Data were collected using semistructured interviews with 12 high school teachers from a Texas school district. Using a priori and open coding, the thematic analysis yielded four themes including that participants: (a) encountered a lack of clear guidelines regarding classroom structure and instruction during SAI implementation, (b) considered current SAI practices insufficient to help students demonstrate proficiency on state exams, (c) identified a need for more targeted information and training to inform their instructional decisions related to SAI, and (d) noted logistical issues as obstacles complicating their ability to manage SAI requirements. Based on these findings, a white paper was created recommending improvements in teachers’ SAI practices and identifying strategies for successfully using SAI and DDDM to foster high school students’ learning in the local setting. When high school students’ learning is improved, they are more likely to persist to graduation and attain college or career readiness, leading to positive social change over time.
Recommended Citation
Smith, Sr., Stephen Courtney, "High School Teachers’ Experiences Using Data-Driven Decision Making to Implement Supplemental Accelerated Instruction" (2025). Walden Dissertations and Doctoral Studies. 18259.
https://scholarworks.waldenu.edu/dissertations/18259
