Resources
This section includes the following resource sections for using data for individualized instruction:
• General
• Privacy
• Interoperability
General
American Association of School Administrators. (2002). Using data to improve schools: What’s working. Washington DC: Office of Educational Research and Improvement.
This report is available from the American Association of School Administrators.
With advances in technology and the increased demand for assessing student learning, an unprecedented amount of data is available to educators. Educators know that the effective use of data can measure student progress, evaluate program and instructional effectiveness, and guide curriculum development.
This document was prepared as an easy-to-read guide to using data to drive school improvement. It provides strategies for building school and district cultures of inquiry, and also describes challenges and successes of educators from a variety of districts.
Bergner, T. and Smith, N. (2007). How can my state benefit from an educational data warehouse? Data Quality Campaign. (ADOBE)
This brief describes an educational data warehouse, discusses the benefits of creating and using a data warehouse, and provides recommendations for designing and implementing a data warehouse from Delaware, Maryland, and Wyoming.
Council of Chief State School Officers (CCSSO) (2007). National Education Data Partnership (NEDP).NEDP is a collaboration of CCSSO, Standard & Poor’s School Evaluation Services, and CELT Corporation. NEDP was formed to help change the way education information is collected, shared, and used by states, educators, policymakers, superintendents, and parents.
Council of Chief State School Officers (CCSSO) (2007). Education Management Advisory Consortium (EIMAC). http://www.ccsso.org/projects/Education_Information_Management_Advisory_Consortium/ EIMAC is providing guidance to states in developing best practice materials to assist with the development of statewide longitudinal data systems.
CCSSO - EIMAC Brief (2007). Connecting Policy and Data: What Legislators Need to Know about State Education Data Systems. http://www.ccsso.org/content/pdfs/EIMAC%20Brief%201.pdf
EIMAC identified five areas of consideration for policymakers regarding the development of statewide longitudinal student data systems: technology; strategic planning; cost; privacy and security; and data quality.
Data Quality Campaign (2007). Building and Using Statewide Longitudinal Data Systems: Implications for Policy. http://www.dataqualitycampaign.org/files/Publication-Building_&_Using_Statewide_Longitudinal_Data_Systems-Implications_for_Policy-040107.pdf
The DQC discusses the policy implications of creating statewide longitudinal data systems, including how legislators can reduce barriers and provide support for building and use these systems.
Data Quality Campaign (2006). Creating Longitudinal Data Systems: Lessons Learned by Leading States. http://www.dataqualitycampaign.org/files/Publications-Creating_Longitudinal_Data_Systems-Lessons_Learned_by_Leading_States.pdf
The DQC reviewed four different states that developed longitudinal data systems (FL, UT, VA, and WI) to determine how they designed the data systems, the cost, any immediate and tangible results, and any “lessons learned”.
Data Quality Campaign (2006). Building Student-level Longitudinal Data Systems: Lessons Learned from Four States. http://www.dataqualitycampaign.org/files/State_Specific-2006_Site_Visit_Synopsis.pdf The DQC reviewed four different states that developed longitudinal data systems (FL, UT, VA, and WI). Information gathered from these states is included in this report.
Data Quality Campaign (2006). Data Use Drives School and District Improvement. http://www.dataqualitycampaign.org/files/Meetings-DQC_Quarterly_Issue_Brief_092506.pdf This brief discuses how to coordinate data to improve performance, how teachers and school districts can use data to adjust instruction to meet students’ needs, and presents case studies from Knoxville, TN, Houston, TX, and Ohio Department of Education.
Marzano, R. J. (2003). Using data: Two wrongs and a right. Educational Leadership, 60 (5), 56-60.
Marzano, R. J. (2003). What works in schools: Translating research into action. Alexandria, VA: ASCD.
Robert Marzano, from the Mid-continent Research for Education and Learning (McREL), comments that, “schools that use data to make decisions are following some of the best advice from both the world of business and the world of education.” He cautions, however, that schools and districts frequently make two key errors in their efforts to be data driven. First, they often use indirect measures of learning – that is, measures that are not sensitive to the actual teaching and learning occurring in the classroom. Second, they often have no plan or system for interpreting and using the data – that is, there is no accompanying explanation of how to improve.
This article identifies 11 school, teacher, and student factors that are the primary determinants of student achievement; and thus, those on which we need to collect and analyze data.
Learning Point Associates (2004). Guide to Using Data in School Improvement Efforts: A Compilation of
Knowledge From Data Retreats and Data Use at Learning Point Associates. http://www.learningpt.org/pdfs/datause/guidebook.pdf This guide is designed for educators who are beginning to learn how to use data in their school improvement planning process. The guide offers information on types of data, strategies for analyzing and understanding data, and methods for determining how these efforts can influence goals and planning.
The guide was originally developed by the North Central Regional Educational Laboratory (NCREL), a wholly owned subsidiary of Learning Point Associates, in collaboration with Judy Sargent, Ph.D., of the Cooperative Educational Service Agency 7 (CESA 7) in Green Bay, Wisconsin, and adapted by Jill Shively of Learning Point Associates.
Pearson Educational Management (LINK). http://www.pearsonedward.com/
Pearson Educational Measurement (PEM) introduces EDWARD™, a statewide assessment-based education data management and reporting system. EDWARD™,
• Enables stakeholders at all levels to make informed, data-driven, education decisions;
• Meets state and federal reporting requirements; and
• Provides student-focused reporting with teacher and organization dimensions (state, district, school, and class).
Peters-Crosby, K. (2007). Using Value Added Data to Make Instructional and Curricular Decisions. http://www.dataqualitycampaign.org/files/Presentations-DQC_Quaterly_Meeting_Using_Value_Added_Data_to_Make_Instructional_and_Curricular_Decisions_031207.pdf
Value-added data measures the academic growth of individual students from year to year.
Popham, J. (2003). The seductive allure of data. Educational Leadership, 60 (5), 48-51.
James Popham from UCLA, states that the most important data in the United States these days are test data – particularly data describing student performance on achievement tests. But he argues that only instructionally beneficial data – from instructionally useful tests – have real value for improving teaching and learning.
Instructionally useful tests are those that measure student attainment of a worthwhile curricular aim; measure something teachable; are based on clear descriptions of the skills and knowledge being measured; yield specific results that can inform teachers about the effectiveness of their instruction; and don’t take too long to administer.
He argues that, if we have instructionally useful tests that provide “per-standard” reporting of results to teachers, then teachers have powerful tools to use to focus instruction and improve student learning of standards.
Raudebush, S. (2004). Schooling, statistics, and poverty: Can we measure school improvement? William H. Angoff Memorial Lecture. Princeton: Educational Testing Service.
This report is available for download or purchase from the Educational Testing Service.
Raudebush discusses two ways of using available test data to judge school effectiveness and improvement – snapshots of average proficiency and value-added systems.
He concludes that, “when high-stakes decisions are based on statistical evidence, it is sensible to scrutinize the quality of the evidence with great care. Holding educators accountable for their contributions to student learning is a laudable goal and one potentially powerful lever for school improvement. But the amount and quality of data must be reasonably aligned with the uses of data in decision making if the accountability initiative is to earn lasting credibility.”
Smith, N. and Steiny, J. (2007). Reporting and analysis tools: Helping mine education data for information riches. Data Quality Campaign. (ADOBE)
This brief discusses the importance of providing user-friendly access to data, and includes nine tips for achieving this goal. It also provides lessons learned from Florida, Hawaii, and Ohio in developing reporting and analysis tools.
Privacy Resources
DQC (2007). Maximizing the Power of Education Data while Ensuring Compliance with Federal Student Privacy Laws. http://www.dataqualitycampaign.org/files/Publications-FERPA_A_Guide_for_State_Policymakers.PDF
This brief discusses the issues surrounding student privacy when creating statewide longitudinal data systems. DQC worked with the law firm of Holland & Knight to analyze Family Educational Rights and Privacy Act (FERPA) and how data collection and access can be aligned with FERPA.
Interoperability Resources
Data Quality Campaign (2007). The Right Data to the Right People at the Right Time: How Interoperability Helps America’s Students Succeed. http://www.dataqualitycampaign.org/files/Meetings-DQC_Quarterly_Issue_Brief_061307.pdf
This brief discusses interoperability and how it improves decision making using data, the current status of interoperability in education, case studies, and lessons learned from other industries.
Schools Interoperability Framework Association (SIFA). Charleston County School District Success Story. http://www.sifinfo.org/upload/story/F21DF3_Charleston%20County[13].pdf
SIFA (2007). Virginia Department of Education Success Story. http://www.sifinfo.org/upload/story/7CD229_VADOE.pdf
SIFA (2007). Wyoming Department of Education Success Story. http://www.sifinfo.org/upload/story/43FZ97_WYDOE.pdf
