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Concurrent Session IX Presentations


Thursday, July 18, 2013
4:15 pm – 5:15 pm


IX–A: Who Moved My EDEN Queries: How to Make the Change From Manual Processes

Joseph Cowan, Pennsylvania Department of Education

    Since 2006, the Pennsylvania Department of Education (PDE) has been collecting data into its Pennsylvania Information Management System (PIMS) for the purpose of accountability reporting. Over the past two years, PDE has teamed with eScholar to use the existing data being collected to simplify and automate EDEN/EDFacts reporting. This session will cover the technologies used and the processes enacted to make the project successful.

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IX–B: Fiscal Coordinators’ Round Table Discussion (Part 2)

Glenda Rader, Michigan Department of Education
Susan Barkley, Kentucky Department of Education
Stephen Cornman, National Center for Education Statistics

    Once a year we submit school district financial data to the National Public Education Financial Survey (NPEFS) and Survey of Local Government Finances: School Systems (F-33). As you complete the survey, you may find those odd little revenues and expenditure items and question, “Am I reporting this correctly?” Here is your opportunity to discuss various financial reporting dilemmas with your colleagues in other states. Bring your questions and answers and be prepared to discuss issues like these: where to code revenue and expenditure categories on the F-33 and NPEFS; when to record various facility acquisition costs as capital vs. contracted services; where to code expenditures incurred by one district but paid on behalf of students in another district without distorting the per-pupil amounts; how to account for Indirect Cost Recovery without distorting actual expenditures; how to record sub-grantee revenue and expenditures so they do not distort the individual or statewide reports; how to record Charter School Operations; how to record Post Employment Benefit cost under new GASB Pronouncements; when to consider an activity a district rather than an agency/student activity; and how various states check district data quality before submitting to the NPEFS and F-33.

IX–C: Collect Once and Use Twice

Sharon Gaston, Texas Education Agency
Shawn Bay, eScholar LLC
Alan Hartwig, Deloitte Consulting LLP

    In 2013, the Texas Education Agency (TEA) will begin the statewide deployment of the Texas Student Data System (TSDS) to more than 1,200 independent school districts that serve nearly 5 million students. The TSDS system will be utilized to meet dual purposes. The TSDS will support TEA’s mandated state reporting and replace a 25-year-old legacy system. At the same time, the system will support the implementation of the StudentGPS Dashboards to teachers and administrators at the local education agencies (LEAs) throughout the state. TSDS will also serve as the primary data collection mechanism for future TEA data collections. This presentation will look at the TSDS architecture and how TSDS will support both data requirements and plans for supporting future data requirements.

IX–D: Illinois State Board of Education (ISBE) and Bloomington District 87: Vision of Real-Time Data Collection and Validation

Jim Peterson, Bloomington Public Schools District 87 (Illinois)
Brandon Williams, Illinois State Board of Education
Gay Sherman and Aziz Elia, CPSI, Ltd.

    The Illinois State Board of Education (ISBE) and Illinois Interactive Report Card (IIRC) are piloting the use of real-time data collection and validation toolsets as a way to gather data from 40 school districts in Illinois. The objective is to allow educators access to data, resources, and tools that will enhance student performance. The project incorporates real-time Extract, Transform, and Load (ETL) and validation options to provide data to a central, cloud-based data store available for Illinois school districts, including data validation and correction, error-reporting services, and a set of analytical tools to allow interoperability between student data, assessments, and other data related to student achievement and learning. Bloomington District 87 will present its vision of the real-time architecture, how it fits in with its current schools interoperability framework (SIF) deployment, and the potential impact this project has on its students and educators. In addition, Bloomington District 87 will discuss its involvement with the inBloom initiative and how it passes data to inBloom through its underlying data center infrastructure IaaS/SaaS pilot called IlliniCloud.

IX–E: Common Education Data Standards (CEDS): 101 Tools and Use

Beth Young, Quality Information Partners, Inc.
Jim Campbell and Nancy Copa, AEM Corporation

    This introductory session will familiarize users with the Common Education Data Standards (CEDS). This session will describe what CEDS are needed, what the parts of CEDS are, and how CEDS can be used. The session will also include a demonstration of both CEDS Tools: Align and Connect.

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IX–F: Kentucky’s Continuous Instructional Improvement System

Maritta Horne, Kentucky Department of Education
Amy King, Pearson

    Kentucky’s Continuous Instructional Improvement Technology System (CIITS) connects standards, electronically stored instructional resources, curriculum, formative assessments, instruction, professional learning, and evaluation of teachers and principals in one place. This session will address how CIITS improves instructional outcomes, teacher effectiveness, and leadership.

IX–G: An Early Warning System in the Yonkers Public Schools

David Weinberger and Shanit Halperin, Yonkers Public Schools (New York)

    The Yonkers Public Schools is establishing an Early Warning System to identify students at risk of not graduating from high school. The system is managed at the district level and is based upon district-specific indicators using local data to provide relevant and replicable information to its schools. This session will present both the components of the system as well as the challenges of implementing data-intensive information to school staff and organizing data use at the district level.

IX–H: Data Lifecycle—Success Strategies From Washington State

Jason Alvarado and Emily Rang, Washington State Office of Superintendent of Public Instruction

    With the recent public release of our Statewide Longitudinal Data System (SLDS), data transparency and data quality have driven better state education agency (SEA) data lifecycle processes. Washington State has more than four unique student information system vendors supporting 296 local education agencies (LEAs) statewide. In this session, Washington State will explain its data lifecycle process, from collection and verification to master-data management and beyond. The presenters will share strategies, technologies, and lessons learned that have proven successful. Future goals will also be discussed.

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IX–I: Data Use for Early Childhood

Missy Cochenour, AEM Corporation
Jaci Holmes, Maine Department of Education
Phil Koshkin, Maryland State Department of Education
Kathryn Tout, Child Trends

    This interactive session will highlight how a statewide longitudinal data system (SLDS) that includes early childhood data uses the data to address key issues in early childhood education, including kindergarten entry assessments, quality rating and improvements systems, child outcomes data, and policy questions. Common Education Data Standards (CEDS) will also be highlighted as a tool that can support the work of addressing these challenges using data.

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IX–J: Major Edit and Imputation Methods Employed in the Processing of Common Core of Data (CCD)

Robert Stillwell, National Center for Education Statistics
Beth Goldberg and Jeff Little, U.S. Census Bureau

    The NCES Common Core of Data (CCD) has employed an array of cleaning techniques in the collection and processing of administrative data. These techniques include data validation checks, editing procedures, and imputation methods. The presenters in this session will explore the various techniques used to process the current CCD data, provide some metrics regarding the efficacy and burden of those procedures, and discuss the ongoing process of improving these techniques to improve data quality and timeliness and to reduce overall program burden from respondent to end user.

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