The Cecil J. Picard Center for Child Development at the University of Louisiana at Lafayette has conducted several program evaluations for the Louisiana Department of Education. Two such program evaluations have focused predominantly on prekindergarten skill development and reading. As a result of these evaluations, several large data sets have been developed and analyzed. Linkage of these data sets with standardized state-level data files like the Student Information System has facilitated sub-group evaluations in the areas of special education, reading, retention, and standardized test scores.

Sessions in Data Use/Data Standards track:

I-C, I-D, I-E, II-A, II-C, III-C, IV-C, IV-H, V-C, V-H, VI-C, VII-A, VII-C, VIII-C, VIII-H, IX-A, IX-C, IX-F, IX-H, X-A, X-C, X-F, XI-A, XI-D, XII-C, XII-D

Using Schools Interoperability Framework (SIF) can enhance and change the district business processes as well as show real time data interoperability, data cleansing, and cost savings at both the district and state levels. A live demonstration of the data extraction and data cleansing process showed how data can be modified in real time for more accurate state and district reporting.

Sessions in Schools Interoperability Framework (SIF) track:

I-B, II-B, III-B, IV-B, V-B, VI-B, VII-B, VIII-B, IX-B

Staff members from the Office of Special Education Programs (OSEP) in the U.S. Department of Education discussed the various program and policy uses of state reported data required under Section 618 of Individuals with Disabilities Education Act (IDEA). They presented examples of data requests, data reports, policy and program implications, and known data limitations.

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Examples of How OSEP Uses SEA-Level IDEA Special Education Data for Program and Policy Purposes (94 KB)

Sessions in Data Use/Data Standards track:

I-C, I-D, I-E, II-A, II-C, III-C, IV-C, IV-H, V-C, V-H, VI-C, VII-A, VII-C, VIII-C, VIII-H, IX-A, IX-C, IX-F, IX-H, X-A, X-C, X-F, XI-A, XI-D, XII-C, XII-D

The University System of Georgia (USG) P-16 Department has developed a unit-level K-20 longitudinal student data warehouse incorporating data from three state education agencies: the Georgia Department of Education, the University System, and the Technical College System. The K-20 student warehouse has been linked to the P-16 Department's longitudinal data system tracking students in teacher preparation programs as they complete their programs, become certified, and enter the teaching workforce. This session described the paths taken in this development. Also discussed were inter-agency collaboration, data governance, and reporting. Plans for extending the systems to Pre-K data were also discussed.

Sessions in Statewide Data Systems (SDS) track:

I-A, III-H, IV-A, IV-D, V-A, VII-D, VII-F, VIII-A, X-H

During this session, state education agencies (SEAs) evaluated their internal controls over data quality. SEAs compared their internal controls to the model of the Committee of Sponsoring Organizations (COSO). That model reviews the control environment, risk assessment, communication, control activities and monitoring.

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Data Quality—Internal Control Assessment (302 KB)

Sessions in EDFacts track:

II-E, III-E, IV-E, V-E, VI-E, VII-E, VIII-E, IX-E, X-E, XI-E

Attendees saw a demo of New Hampshire's solution…New Hampshire has partnered with Performance Pathways to enable teachers, specialists and school district administrators to analyze multiple assessment results for groups of students and individual students. Administrators can run reports to analyze how various groups of students are performing. Teachers can better understand the strengths and weaknesses of their students—across multiple assessments and indicators. Additionally, this solution allows users to create their own local assessments by pulling from an inventory of assessment questions or from their own content. Users can print out bubble sheets and scan the results back into the reporting system. Finally, teachers can create curriculum maps that include lesson plans and search from lessons created by users throughout the country. Many questions can now be asked and considered, for example: are the three fifth grade classes in my school district balanced in terms of student ability? Is a tier II intervention helping our children do better on their assessments? How are our three elementary schools performing compared to each other? Are students who miss ten or more days of school having trouble with their assessments? Which children in my class are having trouble with geometry and measurements? Which students have attendance problems?

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The Initiative For School Empowerment and Excellence (i.4.see) (3.5 MB)

Sessions in Statewide Data Systems (SDS) track:

I-A, III-H, IV-A, IV-D, V-A, VII-D, VII-F, VIII-A, X-H

The Colorado Growth Model provides a common understanding of how individual students and groups of students progress from year to year toward state standards based on where each individual student begins. The model focuses attention on maximizing student progress over time and reveals where, and among which students, the strongest growth is happening and where it is not. The Colorado Growth Model shines a spotlight on the state's most effective schools and districts—those that produce the highest sustained rates of growth in student progress. These schools and districts may or may not be districts or schools with the highest test scores every year. This presentation provided an overview of the Colorado Growth Model and demonstrate a web-based interface currently in use by Colorado school districts.

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Colorado Growth Model: Charting the Course to Post-Secondary
Readiness (1.3 MB)

Sessions in Longitudinal Data Systems (LDS) track:

I-G, II-G, III-G, IV-G, V-G, VI-G, VII-G, VIII-G, IX-G, X-G, XI-G, XII-G

Data quality does not emerge magically from a data warehouse when a report is run. There is a complex, cross-functional, system-wide process that must work to define, collect, store, analyze, and report quality data. The Data Quality Campaign may define ten components that a state should build, but to achieve quality, the contents of the data system must meet established standards. This session took the perspective of four states that are managing their systems for quality data. They described their best practice processes:

1. establish metadata standards,
2. test business rules,
3. certify collections, and
4. produce valid reports.

Sessions in Data Quality track:

II-H, III-D, V-D, VII-H, IX-D, X-D, XI-H

In 1996, the Washington State Legislature recognized the critical role of technology in education and authorized the building of the $55 million K-20 Education Network. Believed to be the first of its kind in the nation, the K-20 Education Network was born of the collaborative efforts of representatives of K-12, community and technical colleges, baccalaureate institutions, the Department of Information Services, the Legislature and private-sector technology providers. This presentation provided insights and observations regarding the network's history, usage, technology, and future plans.

Sessions in Washington State track:

I-I, II-I, III-A, III-I, IV-I, V-I, VI-I, VII-I, VIII-F, VIII-I, IX-I, X-I, XI-I, XII-I