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State Longitudinal Data SystemsSelected Research & Readings (Additional Resources)
 
  STATE LONGITUDINAL DATA SYSTEMS
 
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Race to the Top: Promising Approaches to Establishing Meaningful Data Systems Fostering Continuous Improvement (Goal 2) MS Word PDF - (Jennifer Dounay, Education Commission of the States, March 2009)...

A Policymaker's Guide to the Value of Longitudinal Student Data MS Word - To provide accurate information on school success, it is necessary to follow students over time. Students who have been in the same school for a period of time must be distinguished from those who haven’t. One must be able to look both backward to the academic success of students when they first enter the school and forward to the success of the same students when they leave. This ECS Issue Brief addresses questions and answers policymakers may have about the use and value of longitudinal student data. (Chrys Dougherty, Education Commission of the States, September 2002)...

Data-Driven Decisionmaking PDF - This No Child Left Behind issue brief discusses how districts can support schools' use of data, and how data can be used to make decisions that improve student and school performance. (Education Commission of the States, May 2002)...

A Student-Centered P-16 Accountability Model MS Word - This Briefing Paper from the National Forum on Accountability outlines a "next generation" accountability model that spans states' education systems from pre-kindergarten through the end of undergraduate education (P-16). The author argues that new accountability systems should provide opportunities for all students to prepare well for, and succeed, in college. (Andrea Venezia, Stanford Institute for Higher Education Research, May 2002)...

Getting Smart About Data: Satisfying Federal Reporting Requirements While Helping Schools Improve MS Word - Creating a longitudinal student data system can help states collect the right data to meet the No Child Left Behind (NCLB) requirements and to improve schools. Data collected help educators identify and study best practice, assist in school improvement efforts and objectively evaluate current programs and policies. This ECS Issue Brief provides information on how collecting the necessary information will help states meet NCLB requirements and improve schools. (Chrys Dougherty, Education Commission of the States, September 2002)...

No Child Left Behind Policy Brief: State Information Systems PDF - States’ ability to understand and address the achievement gap depends to a large extent on the design and capacity of their education data systems. This ECS policy brief, which is part of a series examining the impact of the No Child Left Behind Act on state policy, includes a primer on data use, information on longitudinal data systems and key issues for policymakers to consider in assessing the adequacy of their states’ information systems. (Ravay Snow-Renner and Marga Torrence, Education Commission of the States, 2002)...

The Education Data Manager's Guide to the Value of Longitudinal Student Data MS Word - Managers of education data provide a wealth of information to educators, parents and the public in their state or community. This ECS Issue Brief addresses questions and answers data managers may have about the use and value of longitudinal student data. (Chrys Dougherty, Education Commission of the States, September 2002)...

What Makes Data Actionable MS Word - States collect a wealth of data, but what makes that data actionable? Suggestions of how data become actionable are offerred and include: when they can be used to make a comparison, when they are predictive and timely. (Jane Armstrong, Education Commission of the States, July 2002)...

Identifying the Factors, Conditions and Policies that Support Schools' Use of Data for Decisionmaking and School Improvement: Summary of Findings MS Word - This report summarizes the findings of the ECS Clark Foundation project on factors, conditions and policies that support the use of data for decisionmaking and school improvement. Case studies were conducted in five states (California, Colorado, Iowa, Maryland and Texas), including six school districts and 13 schools. Based on the findings, recommendations for policy are being developed. (Jane Armstrong and Katy Anthes, Education Commission of the States, April 2001) ...

2009-10 Indicators Report - This new SREB report provides the first interstate comparisons on the much needed indicators that facilitate the achievement of state's college completion goals. (Southern Regional Education Board, December 2010)...

Making the Numbers Add Up: A Guide for Using Data in College Access and Success Programs - State-by-state college attainment statistics that can serve as a call to action to make higher education more accessible and attainable for all Americans. (Lumina Foundation, December 2009)...

Annual Progress Report on State Data Systems - Each year, the Data Quality Campaign surveys all 50 states, the District of Columbia and Puerto Rico to assess states’ progress toward implementing the 10 Essential Elements of a high-quality longitudinal data system. In 2005, no states reported having all 10 Elements. This year, 11 states have all 10 Elements (up from six states in 2008). (Data Quality Campaign, November 2009)...

On the Cusp in California - Based on examples of pre-k-to-3rd alignment efforts in California, this report calls for the development of longitudinal data systems that would allow teachers from pre-k through third grade to collect data about how children progress through the years and use it to inform quality improvement efforts. Additionally, the author urges the establishment of a pre-k-to-3rd credential that recognizes the unique needs of children at this stage of development and provides teachers with the necessary knowledge and skills to meet those needs. (Linda Jacobson, New America Foundation, October 2009)...

Achieving a Wealth of Riches: Delivering on the Promise of Data to Transform Teaching and Learning - This policy brief addresses why using data represents a significant shift for most teachers in how they perform their jobs, explains the importance of using multiple types of data to affect learning, details the infrastructure necessary to encourage Teachers' use of data, and provides federal policy recommendations. (Alliance for Excellent Education, August 2009)...

Education Data Warehouse Serves Important Function; Project Planning and Management Need Strengthening - Florida's Education Data Warehouse plays an important role by enabling policymakers, educators and researchers to track student progress from prekindergarten through graduate school. However the warehouse has not fully implemented tools to improve stakeholders' access to education data. Recommendations are given for the Florida Department of Eduction to remedy the situation. (OPPAGA, July 2009)...

Using Unique Identifiers to Promote Data Sharing Between Part C and Part B - These two indicators require states to have data systems and procedures in place that allow them to collect accurate student-level data between two different programs that are often administered by different state agencies. Using unique identifiers that are assigned to children identified in Part C and remain assigned to the child as they transition to Part B is one aspect of data sharing that some states have used in order to improve early childhood transition. (Chandra Keller-Allen, NASDSE, April 2009)...

Opportunities to Incorporate Young Child Data into Statewide Longitudinal Data Systems through American Recovery and Reinvestment Act (ARRA) Funding - This paper provides policymakers with ideas for expansion activities. (Charles Bruner and Michelle Stover Wright, Build Initiative, April 2009)...

The Next Step: Using Longitudinal Data Systems to Improve Student Success - Over the next few years the DQC will continue to assist states in developing data systems based on 10 essential elements and in using the information to improve student performance. DQC will focus on two high-priority needs: building demand for the newly available information and helping state agencies assist all stakeholders in harnessing this powerful source of information. (Data Quality Campaign, March 2009)...

Power of Longitudinal Data: Measuring Student Academic Growth - The use of growth models statewide requires that states develop longitudinal data systems to track individual student performance over time. This report identifies the eight essential elements which should be incorporated in the growth model and indicates the policy and practice questions growth models can help address. (Data Quality Campaign, 2008)...

Data Governance: Changing Culture, Breaking Down Silos and Deciding Who Is in Control - The amount and demand for high-quality, accessible education data are increasing and the roles and responsibilities of data managers at the state and district levels are changing. Growing numbers of policymakers are calling for better information on the educational experience and how it relates to other important issues such as child health, welfare, early childhood education and postsecondary success. This policy brief discusses the benefits of data governance for improved data quality and use; key elements for changing culture to leverage technology for successful data governance; and three case studies in implementing data governance strategies. (National Center for Educational Achievement, Data Quality Campaign, August 2008)...

The Third Wave of Longitudinal Data Systems: Data Partnerships - The capacity of state data systems to collect, analyze and provide useful data to inform policymaker and educator decisions has dramatically increased since 2005 when the DQC first surveyed states about the capacity of their data systems. Unless this data is analyzed deeply and used widely to help improve student achievement and outcomes, there will be little need to build robust longitudinal data systems. Some states have begun to work with other entities to make full use of the data. These efforts are described in this report. (Data Quality Campaign, Jane Armstrong, Terry Bergner and Nancy Smith, August 2008) ...

En Route to Seamless Statewide Education Data Systems: Addressing Five Cross-Cutting Concerns - A 2007 workshop involving 11 states and experts in the field identified five core processes that are key to implementing K-16 longitudinal data initiatives: identifying shared benefits for K-12 and postsecondary education sectors, reconciling technical differences between independently created data systems, assuring student privacy, designing systems that enable effective use, and planning for long-term sustainability of state data systems. This report provides a framework for getting started and for addressing these five core issues. (Sharmila Basu Conger, State Higher Education Executive Officers, August 2008)...

Early Childhood Assessment: Why, What, and How - This National Research Council report articulates the latest understanding of best practices in early childhood assessments, leading with the basic principle that the purpose of each assessment should drive decisions about what to measure, how to measure it, and how to use the data. Guidelines for these processes are provided to ensure that the benefits of assessing young children outweigh any negative effects on the children, adults, or programs, such as making children anxious or overburdening teachers. (National Research Council, 2008)...

Teachers' Use of Student Data Systems to Improve Instruction: 2005 to 2007 - Using data from national surveys of teachers and school districts, this brief documents the results of efforts to promote data-informed decision-making within schools. Estimates of the prevalence of K-12 teachers’ access to and use of electronic student data systems at two time points (schools years 2004-05 and 2006-07) are provided. Specifically, the brief addresses three research questions: (1) How broadly are student data systems being implemented in districts and schools; (2) How prevalent are supports for data use and tools for generating and acting on data; and, (3) How are school staff using student data systems. (Lawrence Gallagher, Barbara Means and Christine Padilla, U.S. Department of Education, 2008)...

Critical Connections: Linking States' Unit Record Systems to Track Student Progress - Most students now attend multiple institutions in order to earn a degree and many cross state lines in doing so. As such, constructing a comprehensive picture of longitudinal enrollment behavior requires drawing data from multiple sources and housing these data in a secure environment capable of supporting sophisticated data analyses. This report examined the data contents of state-level student unit records (SURs) and determined that they have the capacity to support such an approach. (Peter Ewell and Marianne Boeke, National Center for Higher Education Management Systems, January 2007)...

Maximizing the Power of Education Data while Ensuring Compliance with Federal Student Privacy Laws: A Guide for State Policymakers - As states build and use more sophisticated education data systems, officials must be cognizant of appropriate protections for the privacy of student records. In particular, the federal Family Educational Rights and Privacy Act (FERPA) imposes limits on the disclosure of student records by agencies and institutions that receive funds from the U.S. Department of Education. This policy brief presents questions and possible approaches to help states balance FERPA requirements and the potential of sophisticated data systems. One consideration, for example, is whether FERPA permits schools and districts, without parental consent, to provide students’ education records to a state longitudinal data system and under what conditions the data can be disclosed. (Data Quality Campaign, 2006)...

Data Use Drives School and District Improvement - Collecting better data to improve schools is essential, but knowing how to analyze and apply the information is just as important. This policy brief discusses how to coordinate different types of data to improve performance and how teachers, schools, districts and states can use longitudinal data. The report also highlights case studies of longitudinal data systems in three districts and identifies other schools, districts and states to watch. Longitudinal data allows the following types of analysis: external and internal benchmarking, program evaluation, understanding relationships and trends, and diagnosis and prescription, according to the author. (Elizabeth Laird, Data Quality Campaign, September 2006) ...

Forum Guide to Elementary/Secondary Virtual Education - This guide provides recommendations for collecting accurate, comparable, and useful data about virtual education in an elementary/secondary education setting. (National Center for Education Statistics, July 2006) ...

What data do districts and schools collect or have access to and how do they use them? PDF - The data that schools and districts collect or have access to can be used to make decisions for improving services for students with disabilities. Data collection can enable districts and schools to better understand the effects of their current practices and address areas that need improvement. This brief examines the various types of data that were available to districts and schools and the way they used the data in the 2002-03 school year. General findings include: (1) although many schools and districts have information about graduation or dropout rates, they lack access to other critical information about students with disabilities; and (2) when districts and schools had access to data, they were more likely to use it as a program evaluation tool than for planning professional development. Findings are detailed in two tables. (Abt Associates Inc., 2005)...

Education's Data Management Initiative: Significant Progress Made, but Better Planning Needed To Accomplish Project Goals - As a condition of receiving federal funding for elementary and secondary education programs, each year the states provide vast amounts of data to the U.S. Department of Education. In the past, this data gathering has presented some problems, including being burdensome on the states. This report contains recommendations for the department of education to improve its data gathering: (1) develop a strategy to help states provide quality data; (2) develop a process within the department to resolve critical, outstanding issues; and (3) develop a clear plan for completing final aspects of the performance-based data management initiative, including specific time frames and indicators of progress toward the initiative’s goals. (U.S. Government Accountability Office, October 2005)...

Forum Guide to Building a Culture of Data Quality: A School & District Resource - The authors introduce the concept of a “culture of quality data” — “the belief that good data are an integral part of teaching, learning, and managing the school enterprise” — and suggest steps schools and districts can take towards developing that culture, through policies and regulations, standards and guidelines, training and professional development, timelines and calendars, technology systems, and a specific data entry environment. One-page tip sheets on the respective roles of principals, teachers, office staff, school board members, superintendents, data coordinators and technology support personnel also are provided. (National Center for Education Statistics, U.S. Department of Education, November 2004)...

Unique Student Identifiers - This Quick Turn Around summarizes information on states’ use of unique student identifiers. For the purposes of this document, unique student identifier is defined as a grouping of numbers and/or letters associated with only one student that is used to identify and track key school data about that student throughout his/her school history. (Terry L. Jackson and Eileen Ahearn, Quick Turn Around, National Association of State Directors of Special Education, May 2004)...

Mapping a Course for Improved Student Learning: How Innovative Schools Systematically Use Student Performance Data To Guide Improvement - This study examines how a handful of innovative schools are using a variety of student performance data to improve the instruction of teachers and the school organization's support for instructional improvement. The authors argue that rather than just relying on one individual test to provide guidance, innovative school leaders are building more comprehensive systems of assessments that provide better interim information from multiple perspectives. (Jonathan A. Supovitz and Valerie Klein, Consortium for Policy Research in Education, November 2003)...

Nine Essential Elements of Statewide Data-Collection Systems PDF - An adequate statewide data-collection system makes it easier to evaluate programs and policies, study best practices, and continuously improve schools. This paper describes nine key elements of an adequate data-collection system and state policy actions. (Chrys Dougherty, National Center for Educational Accountability, 2003)...

Turning Data into Knowledge - Data development must become an active part of school planning and improvement processes, according to this report. It identifies six challenges to building capacity for using data-based decisionmaking. They include: (1) cultivating the desire to transform data into knowledge, (2) focusing on a process for planned data use, (3) committing to the acquisition of data, (4) organizing data management, (5) developing analytical capacity and (6) strategically applying information and results. (William Clune and Norman Webb, Wisconsin Center for Education Resources, Highlights, Winter 2001-02)...

Using Data To Think Differently - According to this article from the American Assocation of School Administrators (AASA), school leaders could undertake two initiatives to begin the long process of changing the way public school effectiveness can be assessed: (1) teachers and principals must look at individual student growth over multiple years, and (2) school leaders need to engage the schools, families and community agencies to identify the community’s expectations for public schools. (Robert J. Monson, AASA, December 2002)...

Data Inquiry and Analysis for Educational Reform - This report outlines the most useful types of data for driving the process of school improvement, the steps that must be taken to collect and analyze the data, the role of administrators regarding the data-driven reform process and the results that can be expected. The most useful types of data discussed include: (1) student assessment and demographics; (2) perception data (how the district or school is perceived by students, teachers, parents and community); and (3) school program data. (Howard Wade, ERIC Digest, December 2001)...

How Data Can Help: Putting Information to Work to Raise Student Achievement PDF - The research described in this article is based on interviews with six school districts in five states (California, Colorado, Iowa, Maryland and Texas) that have successfully used data to refocus their improvement efforts. Most districts have designated a person in every school to collect, analyze and report student achievement data. Other qualities the districts share include: strong leadership; support from teachers, students, and parents; use of curriculum specialists for additional support; and flexibility to restructure the school day according to student needs. (Jane Armstrong and Katy Anthes, Reprinted with permission from American School Board Journal, November 2001. Copyright 2001 National School Boards Association. All rights reserved.)...

Building an Automated Student Record System - New reporting requirements and changes in technology have increased the need for managing student data efficiently. This step-by-step guide offers suggestions, checklists and case studies for designing or upgrading automated student information systems. The guide suggests that education agencies use data elements that are: (1) collected on a regular basis, (2) reliable, (3) valid, (4) quantifiable and (5) consistently defined by a recognized body in education. (National Center for Education Statistics, 2000)...

Smart Data: Mining the School District Data Warehouse - A major problem school districts face when analyzing data is the amount of time it takes to compare information from unstructured and diverse resources in order to draw meaningful conclusions. Schools accumulate numerous test scores, discipline reports, attendance records and grades, as well as medical data and demographic information every year. This article profiles the use of data warehousing, a method of data collection previously used only in the corporate world, that is making data collection and interpretation more convenient, accurate and useful. The article includes pricing on some of the systems available, as well as suggestions for shopping for data warehouses. (Lars Kongshem, electronic-school.com, September 1999)...

Using Data for School Improvement - In 1998, the Annenberg Challenge national office sponsored a “practitioner’s conference” that brought together teachers, principals and administrators from disadvantaged schools to learn about accountability. This paper compiles findings from that conference with suggestions for using and interpreting school data. Some suggestions include: (1) having a clear purpose when developing a plan for data collection; (2) carefully choosing assessment tools that are appropriate to the task and aligned with the purpose; and (3) finding resources to help with planning, coordination, collection, interpretation and reporting of data. (Lorraine Keeney, Annenberg Institute for School Reform, May 1998)...

Data Quality: Earning the Confidence of Decision Makers - As more statewide accountability systems rely on databases, concern follows about the quality of the data, the way it is collected and how it is used. This paper explores the foundations of data quality from the formal information systems literature and applies it to the arena of public education decisionmaking. It includes a hierarchy of data quality that ranges from the availability of dysfunctional, bad data to the quality level of data-based decisions made with confidence. It also includes a checklist for determining the quality of existing data sources. (Glynn Ligon, Evaluation Software Publishing Inc., April 1996)...


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