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Concurrent Session VIII Presentations

Wednesday, August 2, 2017
3:00 pm – 4:00 pm

VIII–A Maximizing Data Quality: An Overview of EDFacts Data Quality Strategies

David Lee and Liz Fening, National Center for Education Statistics
Julia Redmon, AEM Corporation

The EDFacts team identifies, collects, and provides data analysis that informs the decisions and policies made by various U.S. Department of Education (ED) program offices. In order for ED stakeholders—both internal and external—to effectively use the data to make decisions, they need to first understand and have confidence in the quality of the data. A thorough data quality review process not only ensures that data collections are timely, complete, and accurate but also safeguards against inconsistent and inappropriate public use of the data. Join us for a discussion about current processes by which EDFacts and ED program offices review data for potential quality issues. The presenters will highlight a new Data Quality Review Process, designed to be replicated across ED programs as part of a complete data quality strategy to ensure high-quality data are available to all EDFacts data users.

Complexity: Intermediate Level

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VIII–B Want to Save Time and Money? Standardize Data and Processes

Larry Fruth, A4L Community
Brian Townsend, Vermont Agency of Digital Services
Mike Reynolds, Cedar Labs
Michelle Elia, CPSI, Ltd.

Every institution that implements a new enterprise application or data system must choose how to integrate it with existing applications. The explosion of educational app, app store, cloud solution, and data analytic providers has placed a premium on high-quality, consistent, and timely student outcomes data. Nearly every provider has its own proprietary data application program interface (API) to exchange learner data collected within that application, which uses its own proprietary API with no common language, data dictionary, or standard set of web services. Openly developed and freely available technical blueprints everyone can build to based on customer demand can streamline your work—but that is only the start. This session will explore what those blueprints are and how you can use them to develop your own customized program to track student data.

Complexity: Intermediate Level

VIII–C Automating the Civil Rights Data Collection (CRDC) and EDFacts Submission Using Ed-Fi and Generate: Save Districts Time, Money, and Effort

Jill Aurand, Nebraska Department of Education
Bill Huennekens, Center for the Integration of IDEA Data, AEM Corporation
Matt Warden, Double Line Partners

Data collection and submission can be cumbersome and time consuming for districts. The Nebraska Department of Education (NDE) recognized that, by leveraging its Ed-Fi implementation and automating data submission, it could support districts in the Civil Rights Data Collection (CRDC) process while also saving its districts time and money. In this session, NDE will show how it is working with Double Line Partners and AEM Corporation to create an automated data submission system, reduce the burden on district staff for state and federal accountability reporting, and realize a tremendous cost saving for each district.

Complexity: Entry Level

VIII–D Interactively Linking the High School Feedback Series Through College Completion

Kate Akers, Devin McGhee, and Scott Secamiglio, Kentucky Center for Education and Workforce Statistics

The Kentucky Center for Education and Workforce Statistics (KCEWS) began a High School Feedback Report (HSFR) for College Going (CG) and College Success (CS) starting in 2010, using data from the Kentucky Longitudinal Data System (KLDS) to link K–12 to postsecondary outcomes. For the first time, KCEWS is expanding its HSFR series to include College Completion (CC) metrics. This new report will provide feedback to K–12 educators on outcomes beyond college going and first-year college success to answer critical questions around college completion. Representatives from KCEWS will present the power of the data linked in this HSFR series using its dynamic reporting tool, Tableau.

Complexity: Entry Level

VIII–E Getting Smart on Social-Emotional Learning Data

Taryn Hochleitner, Data Quality Campaign
Andrew Rice, Education Analytics

Interest continues to grow nationally in supporting students' social-emotional learning (SEL), which can be defined as the knowledge, attitudes, and skills necessary to understand and manage emotions, establish and maintain positive relationships, and make responsible decisions. The CORE Districts, comprised of California's largest school districts, have prioritized SEL and are the first in the nation to include social and emotional factors in school improvement and accountability. This session will focus on several important data considerations for education institutions that are considering or currently embedding this work into their schools.

Complexity: Entry Level

VIII–F A Data-Matching System for Certifications, Education Records, and Employment and Earnings Information

Gardner Carrick, National Association of Manufacturers
Vanessa Brown, National Student Clearinghouse
Javier Miranda, U.S. Census Bureau

The National Association of Manufacturers, in partnership with the National Student Clearinghouse and the U.S. Census Bureau, is building a data-matching system to integrate third-party credential data with student records from credit and noncredit courses at community colleges and then match those combined records with Internal Revenue Service (IRS) tax data and demographic and company census information. This system will provide aggregate student outcome data by program and across a variety of demographic and company characteristics. It will also show the impact of third-party credentials on employment and earnings. Attend this session to learn how schools and state systems can participate.

Complexity: Intermediate Level

VIII–G Transforming Raw Individual Education Plan (IEP) Data Into Datasets Ready for Analysis of Response to Intervention2 (RTI2) Outcomes

Adam Rollins and Eric Oslund, Middle Tennessee State University

This session will describe the processes undertaken to build a comprehensive database containing 6 years of raw data to prepare state-, district-, school-, and subgroup-level datasets for analysis of Response to Intervention2 (RTI2) in Tennessee. Data were compiled by the Tennessee Education Agency in order to establish whether there was a reduction in the rate at which students are identified for a specific learning disability (SLD) under the new statewide implementation of RTI2. The goal was to determine if the new structure and use of the databases were better for understanding initial identification rates compared to prior data management use and practices.

Complexity: Entry Level

VIII–H ED School Climate Surveys (EDSCLS)—Data Collection and Reporting

Isaiah O'Rear, National Center for Education Statistics
Ellis Ott, Fairbanks North Star Borough School District (AK)
Kevin Murphy and Yan Wang, American Institutes for Research

The ED School Climate Surveys (EDSCLS) platform is a no-cost data collection, management, and reporting system developed by the U.S. Department of Education. It contains a suite of school climate surveys for students, staff, and parents; enables schools, districts, and states to conduct their own data collections; and provides statistically sound climate scores and other statistics at the closing of the data collection. The second version of the platform was released in April 2017 and the next version, to be released in fall 2017, will contain national benchmarking information. The panelists will present the platform and share implementation experiences reported by EDSCLS users.

Complexity: Entry Level

VIII–I Taking a Pipeline Approach to Designing Middle School Math Interventions

Meera Garud and Dan Doerger, Hawaii P–20 Partnerships for Education

Hawaii aims to increase the number of students who graduate high school ready to succeed in college-level courses. Hawaii P–20 Partnerships for Education is building on the momentum of a successful 12th-grade-to-college math transition course. Hawaii P–20 has convened a cross-sector workgroup to look farther back in the education pipeline to make sure more middle school students enter ninth grade prepared for success either in algebra 1 (nonscience, technology, engineering, and mathematics [STEM] pathway) or geometry (STEM pathway). This session will discuss how the workgroup is using statewide longitudinal data system (SLDS) data at each step: planning, implementing, and assessing the middle school intervention.

Complexity: Entry Level

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VIII–J Learning to Build a Research Agenda Under a Federated Statewide Longitudinal Data System (SLDS) Model

Jay Pennington, Iowa Department of Education
Jason Pontius, Iowa Board of Regents

Under a 2012 Statewide Longitudinal Data System (SLDS) grant, Iowa created data partnerships across K–12, community college, public postsecondary, and workforce sectors. Iowa uses a centralized SLDS model for reporting and a federated SLDS model for research and evaluation. This session will highlight the state's efforts at establishing a sustainable research agenda through data governance and building trust across data partners. Specifically, the presenters will discuss their first SLDS research project, a math preparedness study that examines issues of math placement and credit transfer across institutions. They will include early examples of how Iowa high schools and colleges have changed practices to improve student success.

Complexity: Intermediate Level

Top

  Room Location
A Palm Court Ballroom Lobby Level
B State Ballroom Lobby Level
C East Ballroom Lobby Level
D Chinese Ballroom Lobby Level
E Virginia Second Level
F South Carolina Second Level
G Rhode Island Second Level
H Pennsylvania Second Level
I Massachusetts Second Level
J New York Second Level