Concurrent Session XII Presentations


Friday, July 10, 2015
11:30 am – 12:30 pm


XII–A: New Approaches to Measuring Teacher Positional Change (Ambient Positional Instability): Lessons Learned From a Multistate Effort

John Weathers, David Taylor, John Baker, and F. Joseph Merlino, 21st Century Partnership for STEM Education

    This session builds upon extensive use of multiple state teacher human resource datasets to assess the degree of teacher Ambient Positional Instability (API). API is the change within/across years (e.g., grade level) in teachers’ assignments. Teacher attrition (e.g., moving schools/districts) is captured as part of API, but API is focused on within-school movement currently not tracked by states/local education agencies (LEAs). API likely has strong negative impacts on program implementation, school culture, and student achievement. This session offers lessons learned and real examples of data collection structures, variables, and storage considerations necessary to accurately track and report API to schools and LEAs.

    Complexity: Intermediate Level

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XII–B: Digital Resource Tagging—Fundamentals for Effective Sharing

Layla Bonnot, Council of Chief State School Officers
Angela Baker, Georgia Department of Education
Jim Goodell, Quality Information Partners, Inc.
Don Ginder, CELT Corporation

    The most important elements for sharing digital resources are clear agreements on a vocabulary for tagging resources and confidence in the reliability of other organizations in the vetting process. Hear how a number of states have developed a standard tagging vocabulary in alignment with multiple standards organizations, including the Common Education Data Standards (CEDS), the Learning Resource Metadata Initiative (LRMI), and the Dublin Core Metadata Initiative (DCMI). Find out how your state can join the collaboration. Also see how this work has influenced the technical specifications for the K–12 Open Educational Resources (OER) Collaborative Request for Proposal (RFP) for developing open resources that will offer additional choice to local education agencies, significantly reduce expenditures for instructional materials, and provide much greater flexibility with quality educational content.

    Complexity: Entry Level

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XII–C: The Business of Data: Employer Engagement With Education and Workforce Information

Rachel Zinn, Workforce Data Quality Campaign
Jamie Francis, U.S. Chamber of Commerce
Catherine Imperatore, Association for Career and Technical Education (ACTE)

    The business community can be an important ally in supporting and improving P20W data systems. During this session, we will explore several ways that employers are using data. The Workforce Data Quality Campaign will give examples of state engagement with longitudinal data systems, the U.S. Chamber of Commerce Foundation will present a model for Talent Flow Analysis, and the Association for Career and Technical Education will describe a project linking education records with industry certification data.

    Complexity: Entry Level

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XII–D: Considering Multiple Assessments to Improve Instruction

Cyndy Currier, New Hampshire Department of Education
Brenda Dorrell, Delaware Department of Education

    Teachers and specialists use multiple assessments measures to inform instruction (e.g., Dynamic Indicators of Basic Early Literacy Skills [DIBELS], Northwest Evaluation Association [NWEA], STAR, State Assessments, Fountas and Pinnell, local assessments, etc.). Learn about the process of setting up an assessment framework and consider the important factors as teachers access this data to help students. Learn how New Hampshire and Delaware are helping schools analyze assessment data to inform instruction. Using the PerformancePlus system, teachers can easily see reports on how their students perform and even place students in interventions and progress monitor them.

    Complexity: Entry Level

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XII–E: Statewide Data Access for Application Providers—The West Virginia Way!

Sterling Beane and Randall Kirk, West Virginia Department of Education
Larry Fruth, SIF Association/A4L
Jason Wrage, Ovrtr

    How do you address the needs and demands from more than 100 different applications providers needing data from your statewide student information system (SIS)? This session will focus on how the West Virginia Department of Education is using the SIF xPress Roster to gather and control data efficiently and economically.

    Complexity: Intermediate Level

XII–F: Strange Bedfellows: Linking Multiple Types of Universe Data for Local Education Agencies (LEAs) and Schools

Marie Stetser, National Center for Education Statistics

    The Common Core of Data (CCD) is the National Center for Education Statistics’ (NCES’) primary database for the public elementary and secondary education universe of local education agencies (LEAs; i.e., school districts) in the United States. It consists of two primary types of data: (1) nonfiscal data reported through EDFacts, which include institutional characteristics, aggregate information describing student demographics, staff resources, diploma counts, and dropout counts; and (2) fiscal data reported separately through surveys of public education finance surveys, including the National Public Education Finance Survey and the School District Demographic Survey. Every two years, the U.S. Census Bureau collects a list of school districts from states for the School District Boundary Review Program. This list becomes an important component used to create poverty estimates at the school district level through the census’ Small Area Income and Poverty Estimates program. Increasingly, these data are being used together for high profile policy analysis. However, when we look closely at what happens when we link the IES universe survey nonfiscal data reported in EDFacts with fiscal data reported through fiscal surveys and with poverty estimates by school district, we begin to see that these data sets do not provide a simple or clean match of data. This session will highlight some of these discrepancies and their impact, and provide an opportunity for discussing ways to improve the consistency of universe survey data for schools and LEAs.

    Complexity: Intermediate Level

XII–G: The Value and Impact of Open, Common Data Definitions for Student Success

Beth Davis, PAR Framework
Denise Nadasen, University of Maryland University College
Joshua Riedy, University of North Dakota

    This session will show how Predictive Analytics Reporting (PAR) Framework’s open, published, common data definitions and data gathering, handling, and analysis resources are helping to standardize meanings for metrics that predict points of student loss in the U.S. higher education ecosystem. PAR works with a heterogeneous set of U.S. institutions to enable effective and meaningful outcomes comparisons that reflect the changing landscape of educational models. This session will provide initial insight into the performance of alternative delivery models, including online learning and especially with regards to transfer students and adult learners.

    Complexity: Intermediate Level

XII–H: Corn and Corndogs: New Developments in Rural Classifications and Locale Boundaries

Doug Geverdt, U.S. Census Bureau

    Many federal education programs rely on rural classifications to target resources and determine program eligibility. Although programs may share a need to identify rural schools, the geographic criteria used by the programs may vary. This presentation will briefly review the rural criteria used for E-Rate, the Rural Education Achievement Program (REAP), and the National Center for Education Statistics (NCES) locale classifications. It will discuss recent and future changes to program definitions and the practical impact of those changes on states, districts, and schools. The discussion will also introduce new spatial data resources to help visualize and analyze the geographic context of schools and districts.

    Complexity: Entry Level

XII–I: Connecting Data Systems and Data Providers to Reconnect Youth

Laura Maurizi, Isaac Hammond-Paul, and Kilin Boardman-Schroyer, District of Columbia Office of the State Superintendent of Education

    Part I of this session will describe how the District of Columbia (DC) Office of the State Superintendent of Education (OSSE) was able to supplement K–12 data from its statewide longitudinal data system (SLDS) with data from the National Student Clearinghouse, GED Analytics, and DC providers of Adult Basic Education to identify a population of approximately 7,500 disengaged youth in DC. Part II will detail how the newly established DC ReEngagement Center has formed collaborations and data-sharing agreements with other state agencies, local police, and local NGOs to develop a provider database, conduct outreach, reconnect, and track outcomes of disengaged youth in DC. Challenges and successes will be discussed.

    Complexity: Entry Level

XII–J: Are We Where We Are Most Needed? Education, Community Needs, and AmeriCorps

Robin Ghertner and Joseph Breems, Corporation for National and Community Service
Katie Seely-Gant, Energetics Technology Center

    Since AmeriCorps is a place-based program, organizations applying for AmeriCorps funding must demonstrate that their programs address a specific need in their community. This session will present the first systematic study of this type, designed to assess the alignment between indicators of educational needs—including graduation rates, educational attainment, and lowperforming schools—and AmeriCorps grant allocation. Drawing on data from Common Core of Data (CCD) and other sources, researchers applied a spatial probit model using Bayesian Markov Chain Monte Carlo (MCMC) methods and controlled for various community factors. In general, the location of AmeriCorps grantees has a moderate alignment with educational need. Implications for AmeriCorps and other federal grant programs will be discussed.

    Complexity: Intermediate Level

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