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EdD Capstone Project Study: High School Teachers’ Experiences Using Data-Driven Decision Making to Implement Supplemental Accelerated Instruction (HB 4545) – Walden University ( Update)

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Complete your doctoral capstone proposal with this comprehensive EdD project study – updated for the 2026–2027 academic year! This document presents the full proposal for a Doctor of Education (EdD) project study by Stephen C. Smith, Sr., submitted to Walden University in May 2025. The study explores the struggles of high school teachers in a suburban Texas district using data‑driven decision making (DDDM) to implement supplemental accelerated instruction (SAI) as mandated by Texas House Bill 4545 – a law requiring 30 hours of targeted tutoring for every student who fails to demonstrate proficiency on STAAR exams. What’s included inside: Complete abstract, problem statement, rationale, and research question – RQ: What are the experiences of high school teachers using DDDM to implement SAI for students who failed STAAR exams? Conceptual framework – Mandinach et al.’s DDDM model (data → information → knowledge continuum). Comprehensive literature review – covering DDDM, standardized testing, personalized learning (individualization, differentiation, personalization), learning/instructional loss (faucet theory, COVID-19 impact), high‑dosage tutoring, data literacy, collaboration, and school data culture. Methodology – basic qualitative design, purposeful sampling (12‑15 participants), semi‑structured interviews, responsive interviewing techniques. Data collection & analysis plan – a priori, open, and axial coding; thematic analysis; member checking; credibility and trustworthiness procedures. Ethical protections – informed consent, confidentiality (alphanumeric codes), data security, IRB compliance. Detailed interview protocol – 16 open‑ended questions aligned to the DDDM framework, screening questions, opening/closing scripts. Appendices – participant invitation letter, informed consent form, interview protocol. References – 100+ peer‑reviewed sources (APA format). Why this guide works: Perfect for EdD students, educational leadership researchers, and policymakers interested in data‑driven instruction, state‑mandated tutoring programs, and improving STAAR outcomes. Use it as a template for your own capstone proposal, a literature review resource, or a model for qualitative methodology in education. Note: This is a complete proposal draft. Always follow your university’s current guidelines and obtain IRB approval before data collection.

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Abstract

High School Teachers’ Experiences Using Data-Driven Decision Making to Implement

Supplemental Accelerated Instruction

by

Stephen C. Smith, Sr.



BA, Midwestern State University, 2002

MS, Walden University, 2018




Proposal Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Education




Walden University

May 2025

, Abstract

A suburban Texas district implemented supplemental accelerated instruction (SAI)

according to Texas’ House Bill 4545 to assist students who failed to demonstrate

proficiency on State of Texas Assessments of Academic Readiness (STAAR) exams.

However, district high school teachers struggle to use data-driven decision-making

(DDDM) with SAI to improve student proficiency on STAAR exams. The purpose of the

study is to explore high school teachers’ experiences using DDDM to implement SAI for

students who failed to demonstrate proficiency on STAAR exams. The conceptual

framework is Mandinach et al.’s theory of DDDM. The research question addresses

teacher experiences using DDDM with SAI to support students below proficiency on the

STARR exam. Data collected for this basic qualitative study will come from 12-15

semistructured interviews of teachers experienced with SAI. The semistructured

interview data will be analyzed thematically using a priori, axial, and open coding to

identify teacher themes about their experiences. The study’s findings may lead to local

positive social change by identifying potential barriers to student STAAR proficiency,

resulting in improved educational outcomes and graduation rates.

,High School Teachers’ Experiences Using Data-Driven Decision Making to Implement

Supplemental Accelerated Instruction

by

Stephen C. Smith, Sr.



BA, Midwestern State University, 2002

MS, Walden University, 2018




Proposal Submitted in Partial Fulfillment

of the Requirements for the Degree of

Doctor of Education




Walden University

May 2025

, Dedication

This project study is dedicated primarily to my family. To my wife Christy, thank

you for all the support, and—more importantly—the patience you provided from the

beginning of this journey as well as your belief in me that sustains me daily. To my

children, Sache, Stephen, and Elijah: you are my inspiration. I also hope my work is the

beginning of an educational and aspirational legacy for my grandchildren, Ellyjah and

Carlee-Symone; I hope they learn from me that hard work pays off! To my mother,

Dorothy, making you proud has always been the driving force in all my endeavors since

childhood, and I thank you for passing down your work ethic and determination. To my

fellow teachers and coaches: your help, advice, and understanding throughout my masters

and doctoral journey is much appreciated and helped immensely while I navigated the

roles of teacher, coach, and student. Lastly, thanks to Coach Kenith Pope, the dean of

running-back coaches on the collegiate level: although we have never met, the idea of

pursuing a masters and later a doctoral degree while coaching and teaching began with

your introduction at the 2017 Texas High School Coaches Association convention in

Houston, Texas.

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