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STATS-DC 2010 NCES Data Conference
 

Concurrent Session V Presentations


Thursday, July 29, 2010
9:45am–10:45am


 
V–A Using SLDS Data to Improve Student Achievement—The Maine Growth Model and At-Risk Students Data Marts
William Hurwitch, Maine Department of Education
Manos Stefanakos, Choice Solutions, Inc.
    Maine will demonstrate how two key data marts from its statewide longitudinal data system (SLDS) data warehouse provide data to address the questions and concerns of parents, teachers, administrators, and researchers. In this session, the Maine Growth Model will be used to examine student and school achievement and growth over time utilizing state assessment data.  The At-Risk Students data mart will use data analytics to examine dropout factors to augment intervention.

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V–B Traveling Through Time:  The Forum Guide to Longitudinal Data Systems
Bruce Dacey, Delaware Department of Education
Anthony Garofano, Quality Information Partners, Inc.
    By facilitating the collection and use of detailed, high-quality student- and staff-level data linked over time, longitudinal data systems (LDSs) hold the promise of revolutionizing the way we educate our students. Traveling Through Time: The Forum Guide to Longitudinal Data Systems is intended to help state and local education agencies meet the many challenges involved in building robust LDSs. This session will use the guide to answer key questions ranging from “What is an LDS and what does an ideal one look like?” to “Why are data governance and system planning so critical?”

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V–C

Closing the Gaps for English Language Learners: Measurement and Cost
Lori Taylor, Dennis Jansen, and Timothy Gronberg, Texas A&M University

    This session will report on a research project that has two interrelated parts. First, the researchers use existing data on individual student performance in Texas to develop more accurate measures of academic progress and performance for Limited English Proficiency (LEP) students. The current measures of LEP student performance are inadequate to the task, because students who pass the English reading/English Language Arts TAKS test are, by definition, no longer LEP students. Therefore, any student who succeeds academically (at least in this dimension) is removed from the category of LEP students, and any measure of student performance that is based on the student’s current LEP status is biased downward. Second, the researchers use those newly developed performance measures to estimate the cost of closing the achievement gap between LEP and non-LEP students. This cost-function based analysis generally follows previous work on the cost of education in Texas (Gronberg et al. 2004, 2005). The primary innovation of this analysis is the incorporation of the refined student performance measures developed in Part 1 of the project. This analysis provides the first estimates of the marginal cost of serving LEP populations that are based specifically on separate performance estimates for LEP students.
 
V–D

Embedding Cultural Change Into Your ProjectAppreciative Inquiry and the Excecutive Workshop
Janice Gunnip, Greenwich Public Schools (Connecticut)
Randolph Thomas, U.S. Virgin Islands Department of Education
NonaUllman, Improve, LLC

    Learn how the U.S. Virgin Islands and Greenwich Public Schools used one-day Executive Workshops based on the principles of Appreciative Inquiry (a modern theory of change management) to kick off their longitudinal data warehousing projects to engage, inspire, and energize all participants. Using Appreciative Inquiry at project onset is a proven, positive, collaborative approach to developing a project’s shared vision, goals, values, design, and to prioritizing action. Appreciative Inquiry is a way of building on the core competencies and prior successes of people in the organization to create the cultural change required to achieve project goals.

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V–E

Workshop: A District’s Approach to Using Data to Improve Educational Outcomes (Part II)
Mwarumba Mwavita, Joe Kitchens, and Lisa McLaughlin
Western Heights Public Schools (Oklahoma)

    Educators at all levels—from local classrooms to district offices to state and federal education agencies—must recognize that true school improvement, the type that is lasting and meaningful, will occur only when school systems and agencies are simultaneously supported via interdependent, classroom-driven, longitudinal data systems that provide near real-time, appropriately aggregated/disaggregated data to students, teachers, parents, and other stakeholders, including state and federal agencies. This workshop will demonstrate how a school district’s longitudinal data system (LDS) has impacted student learning, instruction, and school culture toward a focused agenda–that of learning.
 
V–F

EDFacts Data Quality Improvement Program—Data Quality Assessment and Metadata Repository
Barbara Timm and Kevin Sauls, U.S. Department of Education

    EDFacts provides data on K–12 for public reporting and decision-making.  In January 2010, the U.S. Department of Education embarked on a project to improve data quality entitled EDFacts Quality Improvement Program (EQuIP). This project provides a comprehensive look at data quality based on four cornerstones of data quality assessment, data quality improvement process, metadata repository, and data governance. At this session, we will discuss two of those cornerstones—the data quality assessment and metadata repository. The discussion of the data quality assessment cornerstone will include a discussion of data profiling.

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V–G

More for Less! Two States’ Efforts to Reduce Human and Financial Resource Burdens While Increasing Effectiveness and Data Quality Through the Use of Real-Time Data Collections
Barbara Roewe, Oklahoma Department of Education
Hellene Bettencourt, Massachusetts Department of Elementary and Secondary Education
Jim Campbell, SIF Association
Aziz Elia, CPSI, Ltd.

    This session will look at the real-time data collection efforts of the Oklahoma and Massachusetts education departments. Come hear how these two very different states are using a common data collection model to dramatically improve the level of data quality and the timeliness of data collection while realizing substantial cost and resource savings for their local education agencies.
 
V–H

Common Data Standards (CDS):  How to Access the Standards and Provide Feedback
Nancy Smith, National Center for Education Statistics
Beth Young, Quality Information Partners, Inc.
Mark Blevins and Hector Tello, AEM Corporation

    NCES is working with key stakeholders to develop standards for a core set of data elements to ensure that data shared across institutions are consistent and comparable. The Common Data Standards (CDS) Initiative’s goal is to identify a list of key K–12 and K–12-to-postsecondary transition variables (expansion into PreK and the workforce will be considered in the future) and agree upon standard definitions, code sets, and technical specifications for those variables. The CDS Initiative’s Technical Working Group includes participants from state education agencies (SEAs), local education agencies (LEAs), higher education, associations, and the U.S. Department of Education. This session will provide a detailed overview of where to find the standards as well as overviews of the CDS website, use cases, data elements, and technical specifications. A national, collaborative effort, CDS includes participants from SEAs, LEAs, higher education organizations, key non-profit organizations, and the U.S. Department of Education.

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V–I High Data Quality and Bad Statistics:  A User’s Perspective of LDS Systems
Sean Mulvenon, Denise Airola, and Rachel Sutcliffe
University of Arkansas, College of Education and Health Professions
    The development of statewide longitudinal data systems (LDS) has dramatically improved what we can do in education. Many of these systems are receiving awards for “data quality.” However, many problems remain with analyses completed using LDS data. The goal of this session is to outline some of the data quality issues with these systems, present procedures to assess and improve data quality, and ultimately contribute to improved use of LDS systems by educators and researchers.

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