Concurrent Session III Presentations

Wednesday, July 11, 2012
4:30 pm – 5:20 pm

III–A: Title I Allocation Inputs

Ian Millett and Wes Basel, U.S. Census Bureau
Bill Sonnenberg, National Center for Education Statistics

The annual production and use of school-age poverty estimates for the Title I Allocation process is a multi-step project undertaken by the U.S. Census Bureau and the National Center for Education Statistics. This presentation describes the process in some detail, including the biennial update to school district boundaries that represents the functional start of the process and the modelbased procedures used to create the estimates from multiple data sources. NCES will describe the process whereby poverty estimates are combined with other data to derive the actual allocations.

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School District Title I Allocation Inputs: Updating school district boundaries (682 KB)
Small Area Income and Poverty Estimates (SAIPE) Program: Overview and Methodology Update (7.81 MB)

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III–B: From P–20 Data Sources to Business Intelligence Solutions: Data Acquisition, Entity Resolution, Master Data Management, and Comparative Analytics

Kelly Holder, Kansas State Department of Education

The Kansas State Department of Education (KSDE) and the Kansas Board of Regents (KBOR) have collaborated to create a longitudinal P–20 data store. The purpose of the data store is to integrate K–12 data with postsecondary enrollment and completion data for research, analysis, and reporting. Data sources include KSDE, KBOR, National Student Clearinghouse (NSC), Advanced Placement (AP), SAT (Scholastic Aptitude Test), the Kansas Department of Labor (KDOL), and the Department of Defense. This presentation examines the challenges faced by KSDE when creating the P–20 data store and will share information about the lessons learned from each of those challenges.

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III–C: Common Education Data Standards (CEDS): Align and Connect

Beth Young, Quality Information Partners
Jim Campbell, Nancy Copa, and Hector Tello; AEM Corporation

This session takes an in-depth look at how education stakeholders can utilize the available resources of the Common Education Data Standards (CEDS). Participants will learn about the two main CEDS Tools: Align and Connect. The Align Tool allows users to upload data dictionaries and compare the definitions and focuses of collection with other users. A live demonstration will show how the details of alignment and currently available data dictionary comparisons can be used. The Connect Tool allows users to populate a catalog of existing policy and use questions as well as see connections that other users have made and easily adopt and adapt these connections for their own use.

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Common Education Data Standards (CEDS): Align and Connect (1.63 MB)

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III–D: Collecting Reliable Student-Level Career and Technical (C&T) Data and Measuring Its Impact on Student Success

Laura Boudreaux, Louisiana Department of Education
Mark Mossavat, MMCS Consulting, LLC

The State of Louisiana and its partner provide participants with a demonstration of its Career and Technical (C&T) Data Collection, aggregation, and analysis activities. Specifically, it discusses steps taken to ensure data accuracy and reliability as well as how the collected data are utilized not only to meet the Carl Perkins compliance reporting requirements but also to measure the impact of C&T education and activities on student success during and after high school.

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Collecting Reliable Student-Level Career and Technical (C&T) Data and Measuring Its Impact on Student Success (1.14 MB)

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III–E: Adopting Standards Is Easy...Implementation Is the Challenge

Daniel Domagala, Colorado Department of Education

The general consensus is that data standards are good. Recent convergence and evolution of national standards efforts are also generally viewed as a positive trend. So how can state and local education agencies take advantage of these emerging standards without disrupting existing systems or jeopardizing prior investments? This presentation outlines Colorado’s ROI-based decision to migrate towards Common Education Data Standards (CEDS)/Ed-Fi data standards and discusses the ensuing ripple effect and implementation challenges.

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III–F: Linking School and Student Achievement Data From a Large-Scale Database With the Common Core of Data (CCD): Applications for Research

Kelli Cummings, Janet Otterstedt, and Noah VanHorn; University of Oregon

The DIBELS Data System (DDS) is owned by the University of Oregon (UO) and managed by the UO Center on Teaching and Learning (CTL). The DDS contains reading and math achievement data from 19,000 schools (K–6; 1998 to present), across all states. Following up from a 2011 presentation, the presenters describe the intersection of DDS achievement data with the NCES Common Core; report results from a 2012 DDS School Survey to confirm the NCES match and verify enrollment; and share NCES analyses from a subset of schools that have used the DDS for 10 consecutive years.

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Linking School and Student Achievement Data from a Large-Scale Database with the CCD: Applications for Research (3.15 MB)

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III–G: Continuous School Improvement Framework: K–12 Longitudinal Data System (LDS) Data Usage in Hawaii

Justin Katahira, Hawaii Department of Education
Victoria Bernhardt, Education for the Future Initiative, California State University, Chico
Charles Breithaupt, VersiFit Technologies, LLC

Unlock the Power of Data for Continuous School Improvement. Schools can become much more efficient and innovative learning organizations when they use data effectively. When schools use data well, they are able to identify which processes are working and not working to ensure that all students become proficient. Using data to improve all processes enables teachers, collectively, to take their practices to the next level and make a difference for all students.

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III–H: Collaborating in Building a Shareable EDFacts Solution

Tom Ogle, Missouri Department of Elementary and Secondary Education
Steven King, ESP Solutions Group

Typically, individual states have built custom EDFacts solutions. Missouri and South Dakota, working with ESP Solutions Group and based on work from Idaho, are building a solution that can be freely shared with other states. The solution is designed from the ground up to be easily configured and maintained. It uses standard Microsoft SQL technology and includes robust error tracking, process logging, and notification. The system includes monitoring and validation reporting. Once installed, the system can easily be managed, modified, and maintained by a state if it chooses. The solution is built on top of a common unit record set of staging tables aligned with Common Education Data Standards (CEDS) and includes SSIS ETL routines to load these staging tables and creates the aggregate data and files for EDFacts submission. Learn how your state can participate and collaborate in this joint venture.

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III–I: Using R and Longitudinal Data System (LDS) Records to Answer Policy Questions

Jared Knowles, Wisconsin Department of Public Instruction

R is a powerful open source data analysis platform that can be used to analyze, visualize, and statistically model data using best available methods. Examples of real-world policy questions answered using R are presented, as well as a demonstration of reproducible code to replicate these analyses using data from other state education agencies (SEAs) and local education agencies (LEAs).

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Using R and Longitudinal Data System (LDS) Records to Answer Policy Questions (1.58 MB)

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Download Abstract:

Forum Guide to Supporting Data Access for Researchers: A State Education Agency Perspective (29 KB)

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