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Concurrent Session XI Presentations

Thursday, August 3, 2017
10:15 am – 11:15 am

XI–A The Feasibility of Reporting Finance Data at the School Level

Stephen Cornman, National Center for Education Statistics
Cynthia Brown, Rhode Island Department of Education
Kathleen Crain, Arkansas Department of Education
Jim Long, Ohio Department of Education
Chris May, Michigan Department of Education
Kim Morrow, Wyoming Department of Education
Beth Scioneaux, Louisiana Department of Education
Stephen Wheeler, Laura D'Antonio, and Osei Ampadu, U.S. Census Bureau
Lei Zhou, Activate Research

There is a significant demand for finance data at the school level. Policymakers, researchers, and the public have voiced concerns about the equitable distribution of school funding within and across school districts. The Every Student Succeeds Act (ESSA) requires state education agencies (SEAs) and local education agencies (LEAs) to produce report cards that include “the per-pupil expenditures of Federal, State, and local funds, including actual personnel expenditures and actual nonpersonnel expenditures of Federal, State, and local funds, disaggregated by source of funds, for each local educational agency (LEA) and each school in the State for the preceding fiscal year” [Elementary and Secondary Education Act of 1965, as Amended Through P.L. 114–95, enacted December 10, 2015, §1111 (h)(1)(C)(x) and (h)(2)(C)].

The primary purpose of the pilot School-Level Finance Survey (SLFS) is to test the feasibility of collecting school-level finance data in conjunction with the School District Finance Survey (F-33) and the National Public Education Finance Survey (NPEFS). The SLFS is essentially an expansion of the F-33 survey to include some variables at the school level. The pilot SLFS evaluates the collection method to determine if it is a viable, efficient, and cost-effective method to gather school-level finance data and whether this method provides higher-quality data than the previously used methods of collecting these data. This session will describe the challenges inherent in collecting school-level finance data and the response of SEAs, the National Center for Education Statistics, and the U.S. Census Bureau to surmount those challenges.

Complexity: Intermediate Level

XI–B National Assessment of Educational Progress (NAEP) Results Alternate Performance Indicators (API)

Ebony Walton, National Center for Education Statistics
Robert Finnegan and John Hsu, Educational Testing Services (ETS)

Do you want to use National Assessment of Educational Progress (NAEP) data to create your own visualizations? Do you want NAEP data in a machine-readable format? During this session, the presenters will provide an overview of how you can access the NAEP Alternate Performance Indicators (API) online. They will describe the history, query strings, and outputs of the NAEP results API. These results will give you the data in JavaScript object notation (JSON) format, including average scores, percentiles, and percentages of students by any variable publicly available in the NAEP reporting database.

Complexity: Advanced Level

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XI–C Dual Enrollment in Maryland: Using Propensity Scores to Strengthen Program Evaluation With State Longitudinal Data

Angela Henneberger and Alison Preston, University of Maryland School of Social Work

The Maryland Longitudinal Data System (MLDS) Center is legislatively required to submit an annual report on dual-enrollment program participation (simultaneous enrollment in both a public high school and college). Programs such as dual enrollment are often difficult to evaluate causally due to the absence of randomization. Modern causal inference techniques, such as propensity score methods, can be used to strengthen causal inferences in the absence of randomization in education sciences. The presenters will apply this approach to program evaluation using data from the MLDS and discuss the strengths and limitations of using this approach with state longitudinal data systems.

Complexity: Intermediate Level

XI–D Changing How We View Higher Education Data in Tennessee

Brian Douglas, Tennessee Higher Education Commission

Every year, the Tennessee Higher Education Commission (THEC) has several legislative reports that are printed, bound, and hand delivered. The printed documents provide great information, but are static. In 2017, THEC staff began using visualizations to convert traditional reports into interactive charts and maps, providing consumers with more information in an easily digestible and customizable format using Tableau. This session will provide an overview of THEC's new resources and lessons learned during this process.

Complexity: Entry Level

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XI–E Which Came First? The Dashboard or the Population?

Laia Tiderman and Ann Kellogg, Maryland Longitudinal Data System Center

Maryland Longitudinal Data System Center staff found that the current dashboard production process was cumbersome and slow. Center staff began rethinking the development process and asked, “What if we start from the population and not the question?” The center is piloting an alternative process that defines key population groups and that can be used repeatedly to address multiple research questions. This approach requires the center to work with stakeholders to define criteria for “key populations” as well as redesign its dashboard processes. This session will discuss the center's experience with this alternative process and the criteria it used for defining key population groups.

Complexity: Intermediate Level

XI–F Automating Certification Evaluations for State Agencies

Angel Loredo, Maine Department of Education
Dean Hupp, Hupp Information Technologies, Inc.

With the implementation of a new certification system, the Maine Department of Education has fully automated all its teacher certification processes. Come learn how Maine did it; see a demonstration of the new evaluation module; and learn the state's best practices for your possible future certification software projects.

Complexity: Intermediate Level

XI–G Rules of Engagement: Improving Data Governance and Analysis in a Multistate Data System

Sarah Leibrandt and Patrick Lane, Western Interstate Commission for Higher Education
Melissa Beard, Washington Education Research and Data Center
Kate Akers, Kentucky Center for Education and Workforce Statistics

Panelists from the Kentucky Center for Education and Workforce Statistics, the Washington Education Research and Data Center, and the Western Interstate Commission for Higher Education will discuss implications on research and policy of linking data between sectors. Leveraging their experiences with the Multistate Longitudinal Data Exchange, the panelists will explore the following questions: How can data from other states enhance research and evaluation efforts? What data governance issues does this effort raise? What data analysis issues does this effort raise? Participants are encouraged to add their own perspectives around data needs and data governance issues in their states to this lively discussion.

Complexity: Intermediate Level

XI–H Use of State-Representative Data in the High School Longitudinal Study of 2009

Elise Christopher, National Center for Education Statistics
Laura Burns Fritch, RTI International

The High School Longitudinal Study of 2009 (HSLS:09), which is sponsored by the National Center for Education Statistics, is a longitudinal survey of a cohort of high school students who were ninth graders in the fall of 2009. The study focuses on students' trajectories from the beginning of high school into postsecondary education, the workforce, and beyond. The study specifically emphasizes science, technology, engineering, and mathematics (STEM). A distinguishing feature of the study is the ability to generate state-representative estimates associated with public school students in 10 states with augmented samples. This presentation will discuss how these data may be used.

Complexity: Entry Level

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XI–I Data Use to Improve Instruction, Curriculum, and Equity—Consistency, Collaboration, and Communication

Russell Altersitz and Patricia Haney, Logan Township School District (NJ)

In 2016–17, New Jersey schools were provided districtwide grade-level Evidence Statements indicating how each grade level as a whole performed on every Content Standard. This assisted in targeted improvements in district curriculum. Data on individual student performance at the Domain and Cluster levels were also provided to the district. These data were used to help teachers target the needs of their students in the current year and to review students' performance in the previous year to provide data for reflecting on areas in need of improvement. The presenters will provide examples of tools, exercises, and data output used to engage teachers in this use of data to improve instruction.

Complexity: Intermediate Level

XI–J Developing an Open Source Data Standard for Teacher Preparation to Scale Improvement Conversations

Paul Katnick, Missouri Department of Elementary and Secondary Education
Anne-Marie Hoxie, Relay Graduate School of Education
Bryan Richardson, UPD Consulting

A coalition of dozens of teacher preparation programs, several states and districts, and three national foundations have joined together to develop a set of open source data tools to facilitate continuous improvement in the teacher preparation field. Called the “Teacher Preparation Data Project,” this work seeks to chart, from its inception, a course of data use focused on program improvement rather than just accountability. In its development, stakeholders are leveraging the Ed-Fi data standard as well as the Common Education Data Standard to ensure that the tools provide lasting and scalable impact to a community of users. This summer, the project will deliver a free and open source data standard (developed from the existing Ed-Fi standard) as well as a set of data integration tools that will accelerate innovation in the preparation of teachers. Come learn with Relay Graduate School of Education, the state of Missouri, and UPD Consulting how this work was implemented and how to leverage these powerful tools in your community.

Complexity: Intermediate Level


  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