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Concurrent Session II Presentations

Wednesday, July 11, 2011
3:30 pm – 4:20 pm

II–A: District Tools for Understanding and Managing Four-Year Adjusted Cohort Graduation Rates

Bari Erlichson, New Jersey Department of Education
Catrin Davies, Public Consulting Group

    In this session, participants learn about the pathways taken by the New Jersey Department of Education to calculate the four-year adjusted cohort graduation rate, report on it, and educate stakeholders. Participants gain insight to the process for calculating the new measure; the trainings that were developed to support and inform district personnel; the reporting tools that were deployed to help educators identify students who are at risk; and the steps taken to empower stakeholders with an informed understanding of the new calculation.

II–B: High School Rankings by the Media: What We Learned About the Importance of Data Quality in the Common Core of Data and Opportunities for Improvements

Marilyn Seastrom, Acting Deputy Commissioner and Chief Statistician, National Center for Education Statistics
Marie Stetser, NCES Program Director, Common Core of Data, National Center for Education Statistics
Julian Montoya, Nevada Department of Education
John Gonzalez, New York City Public Schools
Robert J. Morse, U.S. News & World Report

    In May 2012, U.S. News and World Report published a High School Rankings report that used several Common Core of Data (CCD) variables from the 2009-10 school data file in the ranking methodology. A student-teacher ratio was also included in the report. Following its publication, several schools that appeared in the rankings refuted the data that appeared in the report. This was followed by many press inquiries to several State Education Agencies, Local Education Agencies, and the National Center for Education Statistics about the quality of the data used. It became obvious that some of the published CCD enrollment and teacher data were inaccurate. This session will discuss the errors that were found in the data, why this happened, and planned improvements to prevent future errors from being included in the report. The session will include discussion from SEA, LEA, and NCES perspectives.

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II–C: What Separates Males and Females? A Multivariate Analysis of the Conditional Effects of Gender and Race/Ethnicity on Postsecondary Enrollment and Attainment

Terris Ross, National Center for Education Statistics

    Numerous studies have documented persistent gaps between the educational attainment of White, Black, and Hispanic males. Further, there is evidence of growing gender gaps within these racial/ethnic groups, as females participate and persist in education at higher rates than their male counterparts (Aud, Fox, and KewalRamani 2010; Aud et al. 2011; Kuh, Kinzie, Buckley, Bridges, and Hayek 2006; Radford, Berkner, Wheeless, and Shepherd 2010; Snyder and Dillow 2011). This study uses logistic regression models (separately by gender and race/ethnicity) to determine the extent to which background variables, achievement measures, math course taking, engagement indicators, and risk factors—such as part-time employment—affect the likelihood of on-time graduation and postsecondary enrollment and attainment for males and females.

II–D: The Bridge Between Data Standards and Learning Standards—Common Core State Standards (CCSS) and Common Education Data Standards (CEDS)

Maureen Wentworth, Council of Chief State School Officers
Jim Goodell, Quality Information Partners
Greg Grossmeier, Creative Commons

    The Common Core State Standards (CCSS) are a bridge to equitable expectations for student learning across state lines. The Common Education Data Standards (CEDS) bridge existing data standards and systems with a common vocabulary for data across the P–20W spectrum. But, what about the connection between the data standards and the content standards? This session addresses how key organizations are working together to bridge what has been a gray area between data standards and content standards. The session demonstrates how CCSS data are contained within CEDS defined elements, including metadata describing relationships between and among competencies, learning resources, and competency-based pathways. The presenters will discuss how the topic is serving as a bridge for collaborative work across other separate initiatives, such as Learning Registry (LR) and the Learning Resource Metadata Initiative (LRMI).

II–E: Is There Value in the Value-Added Data Approach? A Statistical Overview

Elana Broch, Princeton University

    A popular approach to assessing teacher performance is the use of “value-added” data analysis. The technique recently received media attention when the New York City Board of Education was required to publicly release performance data based on this methodology for 18,000 teachers. This technique is an extension of regression, where one or more variables are used to predict an expected value. Performance can then be measured in terms of the deviation from this expected value. This session is a gentle introduction/refresher to linear and multiple regression, culminating in an gentle introduction to value-added data. The pros and cons of using value-added data are discussed, and participants are encouraged to bring examples of the use of value-added data in their states.

II–F: Leveraging Statewide Longitudinal Data Systems for EDFacts Reporting

Joel McFarland, U.S. Department of Education
Ross Lemke, AEM Corporation
Bob Beecham

    This session discusses how states define a “statewide longitudinal data system (SLDS)” in relation to the overall data system and how states leverage their SLDS for EDFacts reporting to the U.S. Department of Education. Our research found that states vary widely in their use of SLDS for federal data reporting. This session shares different state models for utilizing their SLDS for federal reporting.

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II–G: Plan Reviewed…The Journey Begins

Mark Masterson, Arizona Department of Education

    The Arizona Department of Education (ADE) presents its concept of a cloud-based, integrated, statewide education data system that provides the pathway to next-generation learning for all students. ADE proposes its vision of an education maturity model that incorporates current initiatives in data analytics, security, data and curriculum standards, and student success management. ADE shares its challenging beginnings, progress to date, and the valuable lessons learned along the way.

II–H: Getting Free Help: States’ Experiences With the Statewide Longitudinal Data System (SLDS) State Support Team

Corey Chatis, Statewide Longitudinal Data System (SLDS) State Support Team
Peg Votta, Rhode Island Department of Elementary and Secondary Education
Jan Kiehne, Connecticut State Colleges and Universities (ConnSCU)

    The Statewide Longitudinal Data System (SLDS) State Support Team (SST) provides free technical assistance services to all states regarding their planning, implementation, and use of longitudinal data systems. In this session, SST members provide an overview of the technical assistance available, and state staff from Rhode Island and Connecticut discuss their experiences working with the SST on P–20W data warehouse design and data governance efforts.

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II–I: Can We Really Do This? A Story of Multi-State Procurement

Marsha Ward, Ohio Department of Education
Suzan Kinaci, Massachusetts Department of Elementary and Secondary Education

    In this session, representatives from Massachusetts and Ohio share the benefits of and challenges faced during the current multi-state request for proposals (RFPs) effort for a statewide Instructional Improvement System (IIS). The presenters discuss the ups, downs, and lessons learned, as well as share some tips that could help you. Learn how these states made the decision to join together for a single procurement and how you could do the same.