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Concurrent Session V Presentations

Thursday, July 31, 2014
10:15 am – 11:15 am

V–B: Answering the Relevant Questions

Laurel Ballard, Wyoming Department of Enterprise Technology Services
Mitch Johnson, PRE/ETS
Alex Jackl, Houghton Mifflin Harcourt (HMH)

    Grounding policy questions and data outcomes in the reality of how data are used, stored, and moved in the education ecosystem requires a clearly defined process. With today’s anti-data consolidation world, P–20W systems need to address concerns voiced by constituents who are skeptical of a centralized data warehouse while providing valuable and usable information. Wyoming is pioneering a nontraditional P–20W statewide longitudinal data system (SLDS) architecture able to address relevant policy questions while providing all of the access and reporting of a traditional P–20W SLDS, without the centralization of the P–20W dataset. Learn how Wyoming is using a data access layer to develop On-Demand Access to augment the minimum set of stored P–20W data by requiring the system to reach back to agency data systems to pull the required data for reporting.

V–C: Title I Allocations

William Sonnenberg and Stephen Cornman, National Center for Education Statistics
Lucinda Dalzell, U.S. Census Bureau

    The Title I Allocations process involves a vast number of subject matter specialists from various agencies. The extensive data collection and verification of very specialized data elements from both state and local education agencies is an NCES and U.S. Department of Education responsibility. Title I necessitates the development and application of complex mathematical, statistical, and data processing algorithms. NCES has managed these complex processes since the inception of Title I nearly 50 years ago. Since 1997, the annual production and use of school-age poverty estimates has evolved into a multistep project undertaken by the U.S. Census Bureau and NCES. This presentation will describe the allocation process in some detail, including submission dates of state revenue and expenditure data; calculation of state per pupil expenditures (SPPE); the biennial update to school district boundaries that represents a significant functional start of the U.S. Census Bureau process; and the model-based procedures used to create the school-district-level poverty estimates from multiple data sources and the calculation of final allocations.

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V–D: An Early Warning System for You: Wisconsin’s Open Source Predictive Analytic Approach

Jared Knowles, Wisconsin Department of Public Instruction

    In the past two years, Wisconsin has deployed a Dropout Early Warning System (DEWS) for all students in grades 6–9 in the state. During this time, the agency has committed to open sourcing the core components of this system to share with others. The Wisconsin DEWS toolset can be adopted to solve various problems, including those related to predicting assessment scores, college-going, or high school completion. Built on the cutting edge predictive analytics within the R programming language, these tools will help you make better predictions and easily understand the accuracy of the resulting models. This session will provide an overview of the tools and serve as a forum to ask questions about implementing your own flavor of the DEWS.

V–E: Safeguarding Student Privacy: Key Legislative, Technical, and Communication Strategies for States

Dan Domagala, Colorado Department of Education
Neal Gibson, Arkansas Research Center
Rachel Anderson, Data Quality Campaign

    Safeguarding privacy is a critical component of states’ work to use data effectively in support of student learning. But states can meet their responsibility to use data effectually and ethically in a number of ways. Colorado and Arkansas are two states leading diverse efforts in this area. With the recent passage of Colorado HB 1294, a bill to govern the use and protection of student data, Colorado is using policy to ensure that data are used appropriately and that data decisions are communicated transparently to the public. In Arkansas, state data officials are using the structure of the state longitudinal data system itself to manage data securely, reduce data duplication, and ensure student privacy. In this session, moderated by members of the Data Quality Campaign (DQC), Colorado Chief Information Officer Dan Domagala and Neal Gibson, Director of the Arkansas Research Center, will share their states’ strategies for safeguarding privacy and highlight lessons learned for other states.

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V–F: Building Educator Capacity to Use Data: Three States’ Efforts to Support Teachers and Administrators

Robert Swiggum, Georgia Department of Education
Patrick Bush, Delaware Department of Education
Ginny Clifford, New Hampshire Department of Education
Corey Chatis, SLDS State Support Team

    Increasingly, states are focusing on supporting local educators as high-priority users of statewide longitudinal data system (SLDS) data. However, most educators require professional development and support to understand and use data effectively in support of their instructional and administrative decisions. In this session, representatives from the Georgia, Delaware, and New Hampshire Departments of Education will discuss their background and overall approach, the training and professional development they provide (including how they differentiate supports between teachers and administrators), and their lessons learned and effective practices.

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V–G: A Forum Guide on Using Data to Support College and Career Readiness

Lee Rabbitt, Rhode Island Department of Elementary and Secondary Education

    The current focus on preparing all students to be college and career ready requires state and local education agencies to work with one another and partner with higher education and workforce agencies to implement a wide variety of programs and support systems to ensure students are graduating college and career ready. The National Forum on Education Statistics has convened a working group to develop a guide on using data to support college and career readiness. This session will discuss the five data-use cases and related examples that will be included in the guide.

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V–H: Chronic Absenteeism in Hawaii: Tracking and Addressing a Vital Metric for the First Time

David Moyer, Hawaii State Department of Education

    Learn how Hawaii, a state that was late in adopting and calculating chronic absenteeism, has quickly incorporated the metric into policy and practice. This session will introduce participants to Hawaii’s data and will highlight which students in Hawaii are most likely to be chronically absent, some of the factors related to chronic absenteeism, and what effects absenteeism has on student outcomes. In addition to the data, participants will learn how Hawaii is addressing chronic absenteeism from a policy perspective.

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V–I: What Schools of Education Are Doing to Improve Teachers’ Data Literacy: A Deeper Dive

Ellen Mandinach, Jeremy Friedman, and Edith Gummer, Regional Educational Laboratory – West

    This session will describe a study that surveyed schools of education to determine the availability of courses on data use for teachers. The survey also examined state licensure documents to understand if and how data literacy is being addressed by states in their requirements for teacher certification. The session will describe the findings and implications for improving data literacy for teachers.

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V–J: Improving Data Quality Through the Source: Implementing an Interactive Data Quality Curriculum and “Friendly” Data Audits

Patricia Rydlak and Christina Lento, Massachusetts Department of Elementary and Secondary Education

    The Massachusetts Department of Elementary and Secondary Education’s current data verification system includes a series of complex business/validation rules that verify the accuracy of each data element prior to acceptance by the department and certification by each local superintendent. However there is room to significantly improve the quality of the data entered at the source, and it is increasingly important to do this. As we implement a more integrated data system, this system will increasingly be used to target and evaluate the impact of investments to improve instruction and related systems and to inform high-stakes decisions. Hence, the data must be of the highest quality. To achieve this, the department has designed and implemented a data-quality curriculum and audit protocols that will increase data quality at the source. This session will describe this project, which is funded through the federal Longitudinal Data System (LDS) Grant Program and is part of a larger effort to develop an integrated suite of tools for Massachusetts educators.