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A Complete Doctoral Capstone Project Study – Fully Updated for 2026–2027! This comprehensive document is a complete Doctor of Education (EdD) Capstone Project Study submitted to Walden University. It presents a rigorous qualitative investigation into the experiences of high school teachers using Data-Driven Decision Making (DDDM) to implement Supplemental Accelerated Instruction (SAI) under Texas House Bill 4545 for students who failed to demonstrate proficiency on STAAR exams. This capstone serves as an exemplary model for doctoral students, educators, and researchers studying educational policy, data literacy, and instructional interventions in the post-COVID-19 era. What’s Inside? Complete Doctoral Project Study: Includes all sections – Problem Statement, Rationale, Literature Review, Conceptual Framework, Methodology, Data Analysis Plan, and Proposed Project. Conceptual Framework: Grounded in Mandinach et al.'s ,
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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.
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.
Acknowledgments I would like to acknowledge my parents, Kenneth, and Dorothy, as well as my grandparents, George and Frankie, and James and Frances who provided the foundation for me to become successful and, as always, my wife Christy for believing in me. I would also like to thank Drs. Michael Vinella and Amy White for guiding me throughout the project study process. Without you, none of this would be possible.
List of Tables Table 1 Sample Table Title ........................................................................................... 60 Table 2 Another Sample Table Title .............................................................................. 60 The List of Tables above must be updated to reflect any tables in your document. If you do not have any tables, delete this page (including the page break at the end of the page). To update the above List of Tables, you must ensure that you have used the Insert Caption method to label your tables, following the instructions at the end of the Instructions for Using the EdD Capstone Templates document. Once you have done this, to update the List of Tables, RIGHT CLICK anywhere in the List of Tables, select UPDATE FIELD, then select UPDATE ENTIRE TABLE or UPDATE PAGE NUMBERS ONLY, and click OK. This will populate the List of Tables with your table numbers and titles. If you follow this method, the table number and title will come in without a period between them, and there will be a mix of bold, italic, and plain font. Clean up the List of Tables manually by selecting all of the text and removing bolding and italics, then enter a period after each table number and one character space before the table title, as shown in the model in the template. v
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Additionally, if a student retakes a STAAR exam and fails to demonstrate proficiency, that student must complete an additional 30 hours of SAI (TEA, 2021) which could extend the students’ SAI requirement far beyond 150 hours. The SAI in HB 4545 is intended to identify and support students who are behind in specific subjects, providing them with the support and focused attention required to quickly meet or surpass grade-level requirements and demonstrate proficiency on future STAAR tests. In this section of the study, I describe the local problem, rationale, definition of terms, significance of the study, literature review, conceptual framework, and implications. The Local Problem The problem is that high school teachers in a suburban Texas district struggle to use data-driven decision making (DDDM) to satisfy the HB 4545 mandate for specific interventions to target instructional loss. Specific interventions, or targeted instruction in the essential knowledge and skills for the appropriate grade levels and subject area for each student (TEA, 2021), are required to implement SAI for students who failed to demonstrate proficiency on STAAR exams according to HB 4545 requirements. Targeted instruction occurs when teachers respond to their classroom data to reteach, remediate, or extend a skill (Seton Education Partners, n.d.). HB 4545 relies on a one-size-fits-all approach of small-group, intensive tutoring instead of a broader array of student supports and interventions to address students’ learning needs. There may be more effective approaches than a tutoring requirement based solely on time. Sikes and Piñón (2022) suggested that it is important to consider which questions a student missed on the state
exam to provide targeted and personalized support. An approach like this could lead to better outcomes and more effective use of resources. Since HB 4545 requires material used for SAI to be locally created, it is difficult for teachers to address the needs of individual students with only their most recent STAAR exam score in a particular academic discipline as a baseline (School Academic Specialist, personal communication, March 4, 2023). Since an individual student’s STAAR exam score was the only information provided by HB 4545, there is a need for more data. According to Mandinach et al. (2019), educators need data such as demographics, attendance, health, transportation, justice, motivation, home circumstances (i.e., homelessness, foster care, potential abuse, poverty), and special designations (i.e., disability, language learners, bullying) to contextualize student performance and behavior. These other data sources are not intended to replace essential data around student performance, but to provide explanations and context to help educators better understand and interpret what data mean. Therefore, a reliance on test scores and indicators of student performance is inadequate. At one of the district high schools, the School Academic Specialist undertook measures such as triangulating individual students’ Measures of Academic Progress (MAP) and Lexile (reading level) scores from their English classes with their STAAR exam scores to provide more applicable, personalized instruction for each student (School Academic Specialist, September 5, 2022). However, because there is little guidance from the TEA as to how much latitude school districts have in implementing HB 4545, it remains unclear if expanding the
teachers need support to develop the knowledge and skills required to use data for decision-making. Additionally, at the local level, there are no systems in place to track longitudinal data or the ability to collect pieces of data on individual students (School Academic Specialist, personal communication, February 13, 2023). Longitudinal data implies the ability to collect key data, connect all those pieces, and then aggregate across students according to critical variables to analyze the impact and relationship between variables (National Forum on Education Statistics, 2010). Data can provide invaluable information about students' learning strengths and weaknesses as well as clues about personalizing learning to meet those needs. Personalized learning (PL) means shaping instruction to meet student's individual needs, but a vision of PL can only be realized with support for data use (Data Quality Campaign, 2019). The purpose of this qualitative project study was to explore the experiences of high school teachers in a suburban Texas district using DDDM to implement SAI for students who failed to demonstrate proficiency on STAAR exams. Definition of Terms I define the following terms to contextualize their use in the specific setting of this study: Accelerated learning committee : A group established by school districts to develop an educational plan for students who did not perform satisfactorily on the STAAR test (TEA, 2021).
Achievement gap : Significant differences in performance on standardized tests when comparing students of different gender, race, socioeconomic status (SES), and disabilities. (Ratliff et al., 2017). Assessment : Testing tools that assess learning and achievement, influence curriculum and instruction, hold students and their teachers accountable for results, and guide decisions about placement at various levels of education, and inform cross-national comparisons of educational systems (Berman et al., 2019). Data-driven decision-making : The systematic collection, analysis, examination, and interpretation of data to inform practice and policy in educational settings (Mandinach, 2012). Data literacy : The ability to understand and use data effectively to inform decisions (Mandinach & Gummer, 2013). Developmental education : Remedial education courses designed to develop students’ reading, writing, and math skills deemed underprepared for college-level courses (TEA, 2021). Faucet theory : Public schooling creates a flow of resources to all students during the school year that keep all students learning and growing. In the summer, the faucet continues flowing for middle- and higher-income students because of their home environment, but runs dry for lower-income students, who lose access to critical services altogether when the school doors close (Pitcock, 2018).
program effectiveness. The primary purpose of the HB 4545 program is to mitigate learning loss, identify students performing below grade level, and give them focused instruction to meet or surpass grade-level standards (TEA, 2021). Students are presently assigned to SAI with their STAAR exam score in each tested subject as the sole data point by which a teacher must produce a plan for individualized instruction. According to Mandinach and Schildkamp (2021), formal data come with disadvantages, such as that student learning cannot be captured in a single test score, and a test score does not translate into the cause of performance or what to do instructionally. Therefore, the findings from a study exploring the experiences of high school teachers using DDDM to implement SAI for students who failed to demonstrate proficiency on STAAR exams can reveal any potential barriers to performing a primary HB 4545 requirement and lead to administrative changes that improve the program, and, by extension, the educational prospects of the students assigned. The findings from this study may lead to positive social change in my local community by helping educators identify potential barriers to students demonstrating proficiency on STAAR exams, resulting in improved educational outcomes and graduation rates. These actions may lead to more significant job and career opportunities, higher incomes, and better overall student health following graduation. Stakeholders, including students, parents, educators, policymakers, and the local business community, all have a strong interest in ensuring that our educational system can effectively fulfill the needs of all learners. Education is a precursor of change; therefore, educators are responsible for transforming communities and initiating social change (Sharma & Monteiro, 2016).
Research Question Through this study, I will examine the teacher implementation of SAI according to the HB 4545 mandate for specific interventions to target COVID- 19 - related instructional loss. Additionally, I will examine teachers’ experiences and struggles to use DDDM for students who failed to demonstrate proficiency on STAAR exams. RQ 1: What are the experiences of high school teachers in a suburban Texas district using DDDM to implement SAI for students who failed to demonstrate proficiency on STAAR exams? Review of the Literature In this section, I provide a review of the literature of the study. It includes a review of the conceptual framework, how the research was conducted, and a discussion of the broader issue of SAI. Afterward, the major themes of the research—DDDM, standardized testing, PL, learning and instructional loss, and high-dosage tutoring—are examined and defined with subsections on each component. Conceptual Framework The conceptual framework for this project study is Mandinach et al.'s (2008) theory of DDDM, also referred to as data-based decision-making. DDDM is a six-step iterative process arranged in three stages: data, information, and knowledge, each with different associated skills. Data, information, and knowledge form a continuum in which data are transformed into information and ultimately into knowledge that can be applied to make decisions (Ackoff, 1989).
