The monitoring of data within educational institutions is essential to ensure the success of its students and faculty. By continually analyzing data, educational leaders can increase quality and productivity in their institutions. Data Leadership for K-12 Schools in a Time of Accountability explores techniques and processes of educational data analysis and its application in developing solutions and systems for instructional concerns and next-generation learning. Providing extensive research covering areas such as data-driven culture, student accountability, and data dissemination, this unique reference is essential for principals, administrators, practitioners, academicians, students, and educational consultants looking to maximize their institution’s performance.

Chapter 1 presents a summary of literature on the need for more data-driven planning and decision making in schools and districts. The chapter begins with an explanation of how changes in Federal legislation have created a heightened need for data-driven decision-making and have led to increased use of data in education. The second part of Chapter 1 draws on research literature and explains the role of Data Management Systems (DMS) in supporting data-driven decision making (DDM) and positive outcomes for instruction and learning. The last part of Chapter 1 identifies school-level challenges that compromise educators' ability to utilize data. Chapter 2 provides information about the Red Clay Consolidated School District. The chapter begins with a description of Red Clay's student population, schools, budget, and organizational structure. The relationship between Red Clay's performance on State and local assessments and the district's School Improvement Plan (SIP) model is presented. Chapter 2 also describes the DMS currently available in Red Clay and provides an overview of the district's 2006 Strategic Plan and professional development strategies. Chapter 3 focuses on the problem addressed in this Executive Position Paper. The chapter begins with evidence that DMS and DDM are underutilized among Red Clay's School Level Administrators (SLAs) and School Level Educators (SLEs). Next, the purpose of this study and the organizational improvement goals are outlined. The chapter concludes with a description of the sample used in this study and an overview of the data collection strategies employed. Chapter 4 presents recommendations for increasing the use of DMS and DDM among SLAs and SLEs. The recommendations presented in this chapter are focused around three main ideas: increasing access to data, promoting greater use of data, and modifying the district's professional development offerings to support greater use of DMS and DDM. Each recommendation presented in this chapter is supported by data collected in this study. The chapter concludes with a plan for implementing these recommendations. Input from Red Clay staff was used to determine the viability of the proposed plan. Chapter 5 provides a framework for evaluating the recommendations proposed in Chapter 4. Short-term indicators of effectiveness will include increased rates of accessing and analyzing data and greater interest in professional development opportunities related to DMS tools and DDM practices. Long-term indicators of effectiveness will include improved instructional leadership among SLAs, improved instructional practices among SLEs, and greater capacity among all Red Clay educators to use DMS and engage in DDM practices.

People providing services to schools, teachers, and students want to know whether these services are effective. With that knowledge, a project director can expand services that work well and adjust implementation of activities that are not working as expected. When finding that an innovative strategy benefits students, a project director might want to share that information with other service providers who could build upon that strategy. Some organizations that fund programs for students will want a report demonstrating the program’s success. Determining whether a program is effective requires expertise in data collection, study design, and analysis. Not all project directors have this expertise—they tend to be primarily focused on working with schools, teachers, and students to undertake program activities. Collecting and obtaining student-level data may not be a routine part of the program. This book provides an overview of the process for evaluating a program. It is not a detailed methodological text but focuses on awareness of the process. What do program directors need to know about data and data analysis to plan an evaluation or to communicate with an evaluator? Examples focus on supporting college and career readiness programs. Readers can apply these processes to other studies that include a data collection component.

Accountability for Results: The Realities of Data-Driven Decision Making addresses the most salient questions that administrators, school board members, and community stakeholders need to ask to ensure academic and fiscal accountability, providing definitions, background information, and current research. Research on professional development indicates little correlation between spending and student achievement. Teachers are often provided training with little or no monitoring for actual implementation. This lack of follow-up and follow-through minimizes the positive impact on student achievement. Examples are provided of different types of data for the analysis and evaluation of progress in district and school-level improvements. To elicit collaborative discussions and support the development of district planning, reflective questions are also included. Book jacket.

Pt. 1. Theoretical and practical perspectives -- pt. 2. Building support for data-based decisions -- pt. 3. Data-based applications.

Data-driven decision making (DDDM), applied to student achievement testing data, is a central focus of many school and district reform efforts, in part because of federal and state test-based accountability policies. This paper uses RAND research to show how schools and districts are analyzing achievement test results and other types of data to make decisions to improve student success. It examines DDDM policies and suggests future research in the field. A conceptual framework, adapted from the literature and used to organize the discussion, recognizes that multiple data types (input, outcome, process, and satisfaction data) can inform decisions, and that the presence of raw data does not ensure its effective use. Research questions addressed are: what types of data are administrators and teachers using, and how are they using them; what support is available to help with the use of the data; and what factors influence the use of data for decision making? RAND research suggests that most educators find data useful for informing aspects of their work and that they use data to improve teaching and learning. The first implication of this work is that DDDM does not guarantee effective decision making: having data does not mean that it will be used appropriately or lead to improvements. Second, practitioners and policymakers should promote the use of various data types collected at multiple points in time. Third, equal attention needs to be paid to analyzing data and taking action based on data. Capacity-building efforts may be needed to achieve this goal. Fourth, RAND research raises concerns about the consequences of high-stakes testing and excessive reliance on test data. Fifth, attaching stakes to data such as local progress tests can lead to the same negative practices that appear in high-stakes testing systems. Finally, policymakers seeking to promote educators' data use should consider giving teachers flexibility to alter instruction based on data analyses. More research is needed on the effects of DDDM on instruction, student achievement, and other outcomes; how the focus on state test results affects the validity of those tests; and the quality of data being examined, the analyses educators are undertaking, and the decisions they are making.