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Education Recovery and Reinvestment Center

State Fiscal Stabilization Fund (SFSF) Assurances

Our Services

Learning Point Associates offers four core services designed to assist with SFSF data collection requirements:

  • Data Systems to Support Instruction
  • Analytic Support
  • Data Workshops and Coaching
  • Data Systems Tutorials

Data Collection

Updates, New Guidance, and News

Data Collection Requirements

ARRA requires governors to submit applications to the U.S. Department of Education for funds under the State Fiscal Stabilization Fund (SFSF). In doing so, they must provide a number of assurances. The following assurance addresses the collection and use of data, and this section seeks to provide supports to states as they complete their applications. According to ARRA:

The State will establish a longitudinal data system that includes the elements described in section 6401(e)(2)(D) of the America COMPETES Act (20 U.S.C. 9871). [ARRA Section 14005(d)(1)(B)(3)]

The guidance issued by the U.S. Department of Education further clarifies this requirement by directing states to provide assurances that they are working to establish Òpre-K to college and career data systems that track progress and foster continuous improvementÓ (U.S. Department of Education, The America Recovery and Reinvestment Act of 2009).

States currently are building data systems that accommodate records of individual student performance. Unfortunately, many states are designing and developing those systems only to respond to federal or state accountability demands.

Other states are looking beyond accountability demands and are developing the functionality for local districts and school to utilize state data, to combine it with their own formative assessment data, and to use it to drive continuous improvement. Although doing so is much more complex, research indicates that support for periodic, data-driven school-level conversations about improving performance is much more powerful than simply using accountability systems based on annual data. Funds are available for building and improving data systems, and 27 states already are making use of those resources.

This deeper approach also requires the development of tools and training for local practitioners, but it mirrors the same approaches used to make process improvements that has dramatically increased productivity in manufacturing and other industries.

This section provides resources on the following topics:

to help you get started in making the case for comprehensive data systems.

Making the Case for Comprehensive Data Systems

Data Exploration: A Journey to Better Teaching and Learning (video and activity booklet). Using data to improve student achievement is no longer just a good idea, but rather a requirement. This set of materialsprofiles two schools that already operate in a successful data-driven school improvement system. The video highlights sources, analysis strategies, and action that each school has taken as a result of examining data. Included is a supplemental activity booklet that extends the utility of the video through activities and practical data resources.

Data Quality Campaign. The DQCÕs website contains valuable information and resources that help policymakers and practitioners understand the important role that the proper collection and use of data can play in efforts to improve student achievement.

Measuring What Matters: Creating Longitudinal Data Systems to Measure Student Achievement. This report gives an overview of the first phase of the DQCÕs efforts to build support for longitudinal data systems.

State Education Data Systems That Increase Learning and Improve Accountability. This policy publication looks critically at the past, present, and future of how states use their education data systems. It assesses the components needed for system improvements and provides policy recommendations to help states create efficient and useful data systems that contribute to advancing accountability systems and improving student learning.

Building Data Systems

The effectiveness of data systems in education depends on their design, their integration, and their ability to answer critical questions. The following resources can help you get started thinking about the issues.

Effective Use of Electronic Data Systems: A Readiness Guide for School and District Leaders. This guidebook offers tools and resources for district and school leaders who want to acquire and implement an electronic data system. Its discussions and corresponding questions and worksheets prompt dialogue about key issues for readiness, and it provides information on choosing and implementing a data system to guide further investigations.

State Education Data Systems That Increase Learning and Improve Accountability.This policy publication looks critically at the past, present, and future of how states use their education data systems. It assesses the components needed for system improvements and provides policy recommendations to help states create efficient and useful data systems that contribute to advancing accountability systems and improving student learning.

Collecting and Using Data

Once schools have built a data system, they should use the information responsibly. State and school leaders often seek guidance on how best to collect, analyze, report, and apply data in ways that most effectively and fairly inform improvements in teaching and learning. The following resources provide assistance and offer examples of communities that have experienced success in using data to improve student success.

Is the Supply in Demand? Exploring How, When, and Why Teachers Use Research. Learning Point Associates, as part of a grant funded by the Spencer Foundation, explores both the types of educational research that teachers find useful for advancing their instructional practice and the ways that they access the research currently available.

Using Multiple Levels of Data to Address Educational Issues. This Policy Brief summarizes the 2007 Issues & Answers report on evidence-based initiatives that follows.

Getting the Evidence for Evidence-Based Initiatives: How the Midwest States Use Data Systems to Improve Educational Processes and Outcomes. States in the Midwest region are developing innovative approaches to collecting and providing access to high-quality data in order to improve educational decision making. Additional capacity-building and more technical assistance at the state and local levels would enhance this work.

Using Data to Understand the Academic Performance of English Language Learners. The North Central REL developed this issue of Policy Issues to provide perspectives on how information that is already being collected can be analyzed and reported in ways that support the internal information needs of educational systems.

Ohio Data Primer. Learning Point Associates created the Ohio Data Primer in collaboration with the Ohio Department of Education to support districts in their use of data that their states provide. The Ohio Data Primer helps teachers and principals become comfortable thinking about, thinking with, and using data in support of decisions about instruction.

Making Good Choices: A Guide for Schools and Districts(rev. ed.). Learning Point Associates and the Center for School Reform and Improvement have revised this guide to reflect the No Child Left Behind Act of 2001. It now includes an interactive CD-ROM that presents all the tools from the appendices in the print version. It is designed for districts seeking to develop a comprehensive approach to reform and includes many useful self-assessment tools and checklists. The CD-ROM-usable on both Windows and Macintosh platforms-provides assistance to schools and districts in conducting a self-evaluation, profiling a comprehensive reform approach, and making final decisions.

The Next Step: Using Longitudinal Data Systems to Improve Student Success. This new report from the DQC addresses the key issues of (1) the movement from collecting data for compliance to using data for continuous improvement; (2) changing the educational communityÕs culture and maximizing the investments in data; and (3) the implications for policymakers in the accessibility, sharing, and use of data.

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