VIII–A

Deer Park Independent School District, comprising 14 schools, is rolling out a process for automatically validating and certifying student data in their Pearson SASI system. The process detects incomplete, inaccurate, and inconsistent information before the information is reported to the state or is used in district operations. The validations target information contained in the state submission file, but more importantly, they address data in the underlying student information system where it can be corrected by school personnel.

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• Raising the Level of Accuracy and Completeness in Texas District Data Reporting (737 KB)

VIII–B

Ensuring the high quality of instruction and services provided to students is an issue affecting all school systems. In Albuquerque Public Schools (APS), the nation's 34th largest school district with 89,000 students, the Quality Assurance (QA) Program has developed and implemented web-based systems that monitor and report on numerous programs and compliance measures. This presentation explained why APS created and grew its QA processes, and how it uses the district SIS and other data systems to capture and report to school and district staff, especially in the areas of Indian education, special education, language and cultural equity, and student wellness. The future of the APS QA program was also discussed, including evolving business processes, compliance issues, and the technologies used to monitor and report.

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• The Whys and Hows of Implementing a Quality Assurance Program in a Large Public School District (3.05 MB)

VIII–C

Cal-PASS is an initiative that collects, analyzes and shares student data in order to track performance and improve success from elementary school through university. Cal-PASS is a simple and very practical approach that helps educators understand student performance, improve instruction and increase success.

VIII–D

This session was a panel discussion by the U.S. Department of Education (ED) and some states on how they established and maintain data governance. States and ED described the process they use and also identified the barriers and opportunities that emerged.

VIII–E

The Data Model Task Force of the National Forum on Education Statistics is working to create a comprehensive pk-12 data model for education. The purpose of this session was to inform attendees of this effort and to gather feedback and reaction from participants.

The Comprehensive pk-12 Data Model for Education attempts to answer the question: What data do schools, local education agencies (LEAs), and states need to collect and manage to meet the educational needs of their students and the needs of the organization in order to meet those student educational needs?

A single, comprehensive model of education data is prerequisite to establishing automated systems with the right data, data that are comparable across time and systems, and data accurate enough to answer our questions.

Schools and LEAs could use such a model to communicate to vendors their requirements or to classify vendor offerings by the parts of the data model addressed by a particular product. This would allow schools and LEAs to "certify" education applications with respect to relevant parts of the data model and will enhance clarity in the marketplace for product offerings.

A comprehensive local education data model from an LEA perspective could also provide a national standard for schools to evaluate and improve instructional tools, to enhance the movement of student information from one LEA to another, and to inform instruction.

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• The Comprehensive pK-12 Data Model for Education: An Update (596 KB)

VIII–F

This project developed school-level reports from existing district and state data on expenditures, personnel, student demographics, and student outcomes in a single report. The project then created a tool comparing school-level resource allocation practices and student outcomes across all schools within a district over a period of 3 years. This report allows school and district administrators to compare resources between schools or levels of schools (elementary, middle, or high) and allows comparisons of changes within schools and between schools over time. Disproportionate distributions are readily spotted allowing districts to investigate the root cause and rationale for any anomalies.

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• Linking School-Level Resources With Student Outcomes: Comparing Individual School Results Within School Districts (512 KB)

VIII–G

To make the most effective use of data and technology, local and state education agencies must engage in strategic planning and improvement efforts on their core processes, such as curriculum and instruction, assessment, data management, and information technology. With support from the Bill & Melinda Gates Foundation, the Council of Chief State School Officers and the CELT Corporation have developed a framework for use by states and districts to assist in strategic planning and improvement. This framework was developed over the past year, with input and assistance from over 8 states and 24 districts through the Council's Decision Support Architecture Consortium. A key component of the framework is a 'best practices' library. This session provided an opportunity for participants to view the framework and learn about the library.

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• DSACII Strategic Planning and Improvement Framework (767 KB)

VIII–H

This session discussed the recent successes that the Wyoming Integrated Statewide Education (WISE) Data System has had in Schools Interoperability Framework—enabling districts and thus the automation of data collection, formatting, and reporting of school and district data, the ongoing implementation of additional horizontal SIF district applications, and the future of electronic student record exchange in Wyoming. We focused on what we have accomplished last year and what we plan to accomplish for this new school year. The ongoing WISE project not only assists districts in meeting the requirements for the collecting, formatting, and reporting of school and district data as required by Wyoming Department of Education's mandated reports, but also provides the automated ability to efficiently share educational data statewide, from district to district, district to school, and school to school. We also discussed the future of WISE data collections.

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• SIF-WISE Technical Update (415 KB)