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Concurrent Session VII Presentations


Thursday, July 12, 2012
1:30 pm – 2:30 pm


VII–B: Lessons in Successful Educator Data Use From Initial Texas Student Data System Districts

Jami O’Toole, Michael & Susan Dell Foundation
Brian Rawson, Texas Education Agency
Kathy Rollo, Lubbock Independent School District (Texas)

    The Texas Student Data System (TSDS) has been deployed in four districts as part of the phased statewide roll out. This session covers lessons learned about district supports, processes, and training that have led to successful educator use of the TSDS dashboards. Most importantly, participants hear what actions educators are taking based on the frequent, holistic data now available and how districts are defining and tracking success.

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VII–C: Common Education Data Standards (CEDS) Version 3 Standardizing Data to Support Formative Assessment Process Use in School Districts

Nancy Burke, Grafton Public School District (North Dakota)
Lee Rabbitt, Newport Public Schools (Rhode Island)
David Weinberger, Yonkers Public Schools (New York)
Jim Goodell, Quality Information Partners

    The Common Education Data Standards (CEDS) current work has a renewed focus on data elements that support teaching and learning. A specific focus for the K–12 stakeholder group has been the formative assessment process by which teachers and students use data to inform where they need to go (i.e., learning progressions); where they are; and how to close the gap. In this session, local education agency representatives from the CEDS K–12 Stakeholders Group discuss how science research and promising practice models have been used to develop a process model and guiding principles, which serve as the basis for defining data elements and models in CEDS Version 3. Audience feedback on the draft elements and models is encouraged.

VII–D: An Operational Open-Source System for Identity Management and Record Matching

Greg Holland and Neal Gibson, Arkansas Research Center

    The Arkansas Research Center has developed an open-source identity management and recordmatching system, part of Arkansas’ P–20W deidentified brokered system, TrustEd. There are two modules in the system, Knowledgebase Identity Manager (KIM) and TrustEd Identifier Manager (TIM). KIM is a knowledgebase approach to identity matching. TIM takes output from KIM to create an agency-specific identifier, using format-preserving encryption. TIM also provides temporary crosswalks between different agency identifiers for research that require linking two or more agencies’ data. The system is freely available under the FreeBSD license and can be easily modified. The system has currently been benchmarked at 6,000,000 records per hour.

VII–E: Wyoming’s Statewide Data Reporting and Analysis Tool

Leslie Zimmerschied and Joshua McIntyre, Wyoming Department of Education
Sheila Coe and Manos Stefanakos, Choice Solutions, Inc.

    The Wyoming Department of Education (WDE) is moving from being a collector of data for compliance purposes to using its statewide longitudinal data system to become an information service provider. Through the use of statewide data reporting and analysis tools, WDE is seeking to convert district and school data to information usable by superintendents, principals, and teachers. A great deal of statewide collaboration has gone into minimizing the burden on districts to provide data. Working with pilot district technical staff and representatives from the student information system vendors, WDE is utilizing existing data and reports to collect and provide information back to school and districts users on items such as student’s at-risk, assessment information, and student profiles.

VII–F: Utah Data Alliance

John Brandt, Utah State Office of Education
Jennifer Lambert, The University of Utah
Joe Curtin, Office of the Commissioner for Higher Education, Utah System of Higher Education

    As a collaborative, multi-organization partnership, the Utah Data Alliance (UDA) seeks to enhance the quality of educational research and analysis in Utah regarding policies, practices, and programs by utilizing an integrated and confidential statewide longitudinal data system. The UDA provides research findings to policy- and decisionmakers with the goal of improving education and workforce policy and practice. The UDA is comprised of six state agencies contributing data from early learning through the workforce. This session includes background (grant, mission, objectives, partners, stakeholders), data components, design and architecture, agreements and processes, milestones, and current products.

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VII–G: Santa Ana Unified School District—Building Data Dashboards: From Early Warning to College and Career Readiness

Alexandra Ito and Ricardo Enz, Santa Ana Unified School District (California)
Lisa McNicholas, eScholar LLC

    With the aim of supporting student success in high school graduation and college and career readiness, the Santa Ana Unified School District (SAUSD) implemented an “early warning” system using predictive data. SAUSD shares lessons learned in developing early warning data indicators and dashboards as well as integrating an eScholar data warehouse with business intelligence. Results and feedback from the field are also discussed. For the next phase of the project, SAUSD shares its process for gathering requirements for the College and Career Readiness indicators and dashboards and plans for eventual districtwide roll out.

VII–H: Defining Data Literacy for Educators

Ellen Mandinach, WestEd
Edith Gummer, National Science Foundation

    Policymakers emphasize the importance of data use in education, yet the field of data-driven decisionmaking lacks an operational definition of data literacy. Once there is a definition, stakeholders—from professional development providers, to schools of education, to licensing and credentialing agencies—will have a common understanding as they affect change to better prepare current and future educators to use data. This session reports on a conference that brought together diverse experts to discuss and develop the definition. The objective was to define data literacy and identify the knowledge and skills educators use in classrooms, schools, and districts.

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VII–I: Puerto Rico’s Persistently Low-Achieving Schools Data Collection System

Jonathan Hernandez-Agosto, Puerto Rico Department Of Education
Robin Wheeler, PRDE
John Denomy, COGECO Inc.

    Puerto Rico’s persistently low-achieving schools have dramatically reduced the time and effort to produce data-driven improvement plans using a real-time system that collects and analyzes survey, interview, and observation data. The data collected is based on recent research by the Center for Innovation and Improvement on the key performance indicators of successful schools. Its broad-ranging study ensures that schools have the opportunity to consider all relevant factors objectively. The outcome is an improvement plan that is specific to the needs of the school. It also provides districts and the commonwealth the opportunity to identify common challenges.

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