Concurrent Session III Presentations
Wednesday, July 27, 2011
4:30 pm – 5:20 pm
III–A: Privacy Technical Assistance Center (PTAC)–Threats to your Data
Baron Rodriguez, AEM Corporation
Mark Hall, ESS
This session focuses on best practices around securing your data systems through examples from the healthcare,
financial, and defense industries. This presentation will raise awareness of the latest threats to data systems and
what you can do to prevent data breaches through policies, processes, and technical measures.
III–B: Longitudinal Date Analysis–Time Travel for Education Data Fans
Tom Ogle, Missouri Department of Elementary and Secondary Education
Lavan Dukes, Florida Department of Education
Jeff Stowe, Arizona Department of Education
David Weinberger, Yonkers Public Schools (New York)
Patrick Sherrill, U.S. Department of Education
Barbara Clements and Glynn Ligon, ESP Solutions Group
Join us for a longitudinal date analysis of significant events in the history of education data. From the establishment of
the U.S. Department of Education in 1867 to the 2011 Summer Data Conference, this session will place in time those happenings
that shaped how we manage our data. The panelists will not only discuss their personal experiences, but also predict what past
and current trends might predict will happen in the future. The dates of over 50 key events will be provided as context for how
we have reached this point in time. Are we accelerating? Improving?
III–C: Partnership Enhancement Program: Teachers and Institutes for Higher Education (IHE) Faculty Using Data to Plan Professional Development
Barbara Shoemaker, Pam McCardle, and Robert Kegebein; University of Kentucky
This session discusses the evolution of an engaged K–12/Institutes for Higher Education (IHE) partnership program and the ability to adapt the program
to accommodate community and industry needs. The foundation of the Partnership Engagement Project, the collaboration between K–12 teachers and IHE faculty,
begins with the acknowledgment that both parties have an insight and knowledge of math and science educational needs with different perspectives due to their
different expertise. The focus of the collaboration is the improvement of student outcomes for all K–12 students. This goal is reached based on diverse
activities based on the local stakeholders.
III–D: If You Build It, Will They Come?
Amy Sargent, Dianne Tracey, Sue Stein, and Helena Mawdsley;
Center for Technology in Education, Johns Hopkins University
The presenters will discuss the challenges encountered in promoting the use of longitudinal data by state-, district-
and school-level decisionmakers, as well as strategies for overcoming those challenges. The focus of the strategies will
revolve around the use of a practical, needs-based alert protocol incorporating the use of data acquired from Maryland’s
Individuals with Disabilities Education Act (IDEA) Scorecard longitudinal data system. The protocol is currently being used
in the training of Maryland’s K–12 general and special education administrators. It is designed to assist decisionmakers in
identifying students (groups of students and individual students) at risk for school failure and to develop evidence-based
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III–E: Linking Secondary and Postsecondary Data to Measure College Enrollment and Persistence
Laura Holian and Christine Mokher, CNA Education
Deborah Jonas, Virginia Department of Education
This session will address the collaboration between a non-profit research company and the Virginia Department of
Education to estimate college enrollment rates. Presenters will discuss limitations of postsecondary data. Specifically,
the data sets that are used for matching across secondary and postsecondary institutions may not be complete. For example,
matching algorithms are not perfect and may miss true matches. Further, not all institutions are included in data collections.
We will also describe our methods for estimating the undercount of college enrollment rates and how the data have been used
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III–F: Data Standards I: Making Sense of Schools Interoperability Framework (SIF), Common Education Data Standards (CEDS), State Core, and EDFacts
Ross Santy, U.S. Department of Education
Tate Gould, National Center for Education Statistics
Larry Fruth, SIF Association
Alex Jackl, Council of Chief State School Officers
During this presentation, presenters will review the recent efforts of standard development organizations and their
relationships to each other. What these efforts might mean to state and local education agencies will also be discussed.
III–G: How Can NCES Common Core of Data (CCD) Be Used? Live Online Training Session
Stephen Cornman, Patrick Keaton, and Carl Schmitt;
National Center for Education Statistics
Education data provide powerful information for decisionmaking, policymaking, and research within and across
education systems. The Common Core of Data (CCD) is the primary annual database on public elementary and secondary
education in the United States.
This session covers the CCD School, Agency and State files; Agency and State Dropout and Completer files, which are
datasets at the state and agency level that provide valuable counts of completers and dropouts. This session also
presents an overview of CCD Fiscal Surveys including the Local Education Agency Finance Survey (F-33), National
Public Education Financial Survey (NPFES), and the Teacher Compensation Survey (TCS).
Finally, this session offers training on powerful web-based data tools, including the Public School and District
locators, the new Elementary/Secondary Information System (ELSI), and Build-a-Table (BAT). They are tools that
allow the data user to create user-specific tables of CCD public school data by selecting data elements, years,
districts, and schools, among other parameters. This session will also provide an illustration of how to use
large volumes of CCD data to conduct research.
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III–H: Remembering the Importance of Student Effort in Determining Levels of Efficacy in Education Finance Models
R. Anthony Rolle, University of South Florida – College of Education
Despite seemingly positive research results, educational production functions may be predisposed to show weak statistical
relationships in at least two ways because:
More importantly, there is one assumption that typically is ignored but has profound ramifications for any
research involving student learning outcomes and educational productivity: All students are performing optimally
(i.e., students give maximum effort in their pursuit of learning), but no universally accepted determination
of this optimality—or definition of its measurement—exists. As such, the purpose of this presentation is to
reinforce the importance of understanding the influence of student effort in determining educational achievement.
Specifically, the statistical evidence suggests different levels of student effort—when incorporating simulated
data into educational production functions—are associated with different levels of student outcomes. Moreover,
evidence also suggests that student effort can be more important than educational expenditures.
- There is a casualness that surrounds the construction of statistical models used to estimate student learning outcomes
(i.e., multiple statistical models are used), but no universally accepted pedagogical or curricular—and therefore no
mathematical—structure exists for the educational production process.
- There is educational policy research that refers to the significant influences of community, household, and peer
characteristics but no universally accepted definitions for the accurate measurement of these characteristics.
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III–I: A Secured, Free Web-Based Portal for Student Data Confidentiality (Session Cancelled)
Jonathan Hernandez-Agosto, Puerto Rico Department of Education
Orville Disdier and Rodolfo Pagan-Budet, Puerto Rico Institute of Statistics
The Puerto Rico Department of Education (PRDE) is already engaging with the student’s data privacy and confidentiality
assurances. To fulfill the requirements of this challenge, a collaborative agreement was established between the PRDE and
the Puerto Rico Institute of Statistics (PRIS). As a result of this agreement, two goals were accomplished: 1) a student
data confidentiality policy, and 2) a secured, free web-based portal, provided by PRIS and accessible only for the PRDE
statisticians, to put in place better practices for student data privacy. These accomplishments allow the agencies to avoid
unauthorized access and distribution of information.