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Concurrent Session VII Presentations


Wednesday, July 13, 2016
1:45 pm – 2:45 pm


VII–A: Using State Longitudinal Data to Study Postsecondary Success: Lessons From Regional Educational Laboratory (REL) Midwest Research in Two States

Stacy Townsley, Indiana Commission for Higher Education
Kara Arzamendia, Minnesota Department of Education
Amy Proger and Elisabeth Davis, Regional Educational Laboratory (REL) Midwest at the American Institutes for Research

    Panelists will draw on their experience as researchers and practitioners using state longitudinal data systems (SLDSs) to identify practical considerations for studying student access to and success in postsecondary education. Specifically, they will discuss challenges associated with identifying common measures of college readiness and success; tracking students through nontraditional pathways, such as multiple institutions and multiple enrollment spells; and addressing dataquality problems. They will draw on examples from Regional Educational Laboratory (REL) Midwest research conducted in partnership with the Minnesota Office of Higher Education and the Indiana Commission for Higher Education. REL Midwest is funded by the Institute of Education Sciences

    Complexity: Entry Level


VII–B: Two States, One EDFacts Reporting Solution.

John Porter, Mississippi Department of Education
Joseph Cowan, Pennsylvania Department of Education
John Pagnotta, eScholar LLC

    Pennsylvania and Mississippi—two different states with different challenges but both with the same goal of submitting their EDFacts files in a timely and accurate fashion. This session will have a panel of representatives from these two states who use the eScholar EDFacts Solution to generate their EDFacts files. The panelists will address topics such as collecting data from their local education agencies and automating their EDFacts file generations.

    Complexity: Intermediate Level


VII–C: Value and Impact of Common Data Definitions for Scaling Student Success

Nancy O’Neill, University System of Maryland
Hae Okimoto, University of Hawaii System
Ellen Wagner, Hobsons

    This session will explore the impact of using commonly defined variables for conducting predictive analyses to find students at risk, using national outcomes benchmarks for tracking progress, and assessing the efficacy of interventions used to support student success. The University of Hawaii System and the University System of Maryland will share the results of work using the Predictive Analytics Reporting (PAR) Framework’s openly licensed and published data definitions and frameworks for systemic improvements (https://community.datacookbook.com/public/institutions/par). Of particular interest are the findings that common definitions are instrumental to accelerating adoption and to finding solutions and strategies that ensure student success.

    Complexity: Intermediate Level

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VII–D: Data Management System (DMS) Demonstration and Questions and Answers

Robert Stillwell, National Center for Education Statistics
Beth Sinclair, AEM Corporation

    A new tool is in place to process Common Core of Data (CCD) data: the Data Management System (DMS). One of the primary goals of the DMS is to increase the level of transparency in CCD data processing. As soon as data are submitted through the EDFacts Submission System (ESS) and replicated to the DMS, state education agency users can view error reports and provide feedback using the built-in comment system. The CCD Team at Applied Engineering Management (AEM) Corporation have access to the same error reports and can review state comments and explanations in real time. During this session, the AEM CCD Team will demonstrate the DMS, highlight available resources, and provide time for questions. This session is recommended for CCD Nonfiscal Coordinators.

    Complexity: Entry Level

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VII–E: Encouraging Use of the Longitudinal Data System for Research: The Arkansas Experience

Eric Saunders, Arkansas Department of Education
Denise Airola, University of Arkansas
Chrys Dougherty, ACT

    This presentation will discuss how the Arkansas Department of Education encourages the use of longitudinal data for research, with appropriate privacy protections under the Family Educational Rights and Privacy Act (FERPA). The presenters include an out-of-state researcher, the state education agency leader responsible for the data, and a researcher from the state’s land grant university, all of whom are using the data to support the state agency and local education agencies in the state. In addition to describing past and current research, they will discuss future directions for collaborative research agenda development and research dissemination in a world of limited budgets.

    Complexity: Entry Level

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VII–F: Matching External Cohorts to Your Statewide Longitudinal Data System (SLDS)? Beware!

John Sabel, Washington Education Research and Data Center

    The capability to match external cohorts is a vital component of many statewide longitudinal data systems (SLDSs). But there is an important nuance to this capability: the potential for many-to-many matches between a cohort’s research IDs and the SLDS person IDs. Easily overlooked, these many-to-many matches often result in hidden spurious duplication of the SLDS data that are attached to external cohorts. This presentation will show how these many-to-many matches come about and show how these matches can be resolved using a “Crosslink ID.” In addition, some basic measures will be discussed that will show the degree of any many-to-many matches that might exist.

    Complexity: Intermediate Level

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VII–H: Federal K–12 Data at Your Fingertips

Rachel Hansen and Elise Christopher, National Center for Education Statistics
Stephanie Nevill, RTI

    The National Center for Education Statistics (NCES) is responsible for maintaining and providing access to vast stores of student and school data. PowerStats and QuickStats provide users with an intuitive drag-and-drop workspace in which they are able to use many different survey datasets to create complex tables and regressions. This demonstration will highlight K–12 research questions that can be quickly and easily answered using data from the School Survey on Crime and Safety (SSOCS), the High School Longitudinal Study (HSLS), and the Education Longitudinal Study (ELS).

    Complexity: Entry Level

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  Room Location
A Palm Court Ballroom Lobby Level
B State Ballroom Lobby Level
C East Ballroom Lobby Level
D Chinese Ballroom Lobby Level
E Georgia Second Level
F Virginia Second Level
G Chinese Ballroom Second Level
H Pennsylvania Second Level