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Concurrent Session II Presentations

Wednesday, July 30, 2014
3:30 pm – 4:20 pm

II–A: State Fiscal Coordinators Roundtable (Part 2)

Stephen Cornman, U.S. Department of Education
Glenda Rader, Michigan Department of Education
Susan Barkley, Kentucky Department of Education

    This session will facilitate discussion and problem solving among the state fiscal coordinators. Bring your questions, best practices, and “war stories” with you to this session so we can all learn from each other. Topics may include maintenance of effort, indirect costs, chart of accounts, Governmental Accounting Standards Board (GASB) standards, or federal reporting. Knowledge will be shared and valuable network connections will be made.

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II–B: Closing the Data Circle: A Multistate Effort to Create Core Competencies for Educator Data Use (Part 1)

Justin Katahira, University of Hawaii
Christina Tydeman, Hawaii State Department of Education
Marcus Bevier, South Dakota Department of Education
Corey Chatis, SLDS State Support Team

    For nine months, the 13-state Data Use Standards Workgroup has been addressing the question: What do educators need to know and be able to do to effectively use data in support of student learning and success? In this double session, we will describe how we identified the critical knowledge, skills, and professional behaviors that teachers and administrators need in order to use data well. We will share the complete draft product of the effort and host an interactive workshop in which you provide feedback on how the resource can be used and improved, and hear how your state can participate.

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II–C: Mississippi Statewide Teacher Appraisal Rubric (MSTAR)

Karolyn Bridges-Jordan, Mississippi Department of Education
Ross Smith and Martin Disterheft, PITSS America LLC

    Learn how the Mississippi Department of Education (MDE) deployed a new Teacher Evaluation Process based on mobile technology using Application Development Framework (ADF) Mobile. The solution developed for MDE enables principals, educators, and district administrators to conduct teacher evaluations on their PC, iPad, iPhone, and Android phones. The mobile application features offline mode, a user interface driven by server-side configuration, and reference documentation for the entire teacher evaluation process. Learn how the solution was developed and implemented and the long-term educational evaluation roadmap this new mobile platform enables.

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II–D: Factors That Affect Employment Outcomes: Utilizing a Statewide Longitudinal Data System (SLDS) to Develop a Model of Education-to-Workforce Transitions

Charles McGrew, Kentucky Center for Education and Workforce Statistics

    Kentucky is utilizing the K–12, postsecondary, and employment data in its statewide longitudinal data system (SLDS) to analyze the connections between student characteristics, academic preparation, and school-level factors as they relate to college and workforce outcomes. This presentation will include an overview of how the data are connected within the Kentucky Longitudinal Data System, a discussion of how Kentucky goes beyond basic descriptive statistics to conduct policy-driven research with its data, and initial findings from its study.

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II–E: Development, Implementation, and Evaluation of an Early Warning System

Ellis Ott, Fairbanks North Star Borough School District

    A school district in Fairbanks, Alaska, serving approximately 14,000 students used an Early Warning System (EWS) to make changes to an existing graduation success program (GSP). Students in grades K–12 were identified as low, medium, and high risk of dropping out using student data. Graduation success coaches prioritized services with high risk students. The district analyzed the frequency, duration, and quantity of GSP activities with individual students. The development of the EWS model, the implementation of services, and evaluation of the graduation success program will be discussed.

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II–F: Applying a Stakeholder Analysis to Galvanize Your Stakeholder Engagement

Laurel Ballard, Wyoming Department of Enterprise Technology Services
Mitch Johnson, PRE/ETS

    Is your team spending an exorbitant amount of time trying to engage system stakeholders who have little or no interest in the project and even less influence, while spending little time on the stakeholders who really matter? Learn how Wyoming’s P–20W Statewide Longitudinal Data System (SLDS) project is utilizing a detailed stakeholder identification and analysis process to strengthen its stakeholder engagement activities by categorizing stakeholders into the five levels of participation and by planning the engagement activities around the participation levels.

II–G: Georgia’s New Unique Student ID System

Kathy Aspy and Jayesh Dave, Georgia Department of Education

    In January 2014, the Georgia Department of Education implemented the Georgia Unique Identifier for Education, called GUIDE. This new web application has features that have significantly improved the accuracy and timeliness of unique student ID creation and maintenance. Some of the benefits of GUIDE include:
    • Changes to student identity data must be confirmed by the school or district registrar.
    • The matching algorithms are customizable by a state-level GUIDE Administrator.
    • Student identity data is validated in every data collection against the GUIDE database.
    • The GUIDE ensures almost instant access to eight-year student academic history.
    Join us as we share how the new ID application works.

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II–H: Benefits and Challenges of Integrating Data in Early Childhood: The Case of Early Childhood Special Education

Linda Goodman, Connecticut Department of Developmental Services
Abby Winer, The DaSy Center
Kathleen Hebbeler, SRI International

    Young children with disabilities, who often are served in multiple programs, are likely to benefit from more coordinated state efforts to integrate data systems across programs. This session will examine how integrated data systems can improve services for young children with disabilities, by, for example, allowing states to answer critical policy questions such as, “Are children who receive early intervention or preschool special education services less likely to need special education services later on?” Panelists will discuss the issues states face when trying to link early childhood data systems, using early intervention and preschool special education programs as an example.

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II–I: Maximizing NCES Resources to Develop ANSWERS, Alaska’s P–20W Statewide Longitudinal Data System (SLDS)

Kerry Thomas and Stephanie Butler, Alaska Commission on Postsecondary Education

    Alaska’s approach to maximizing the FY12 Statewide Longitudinal Data System (SLDS) grant to develop ANSWERS P–20W SLDS by leveraging NCES services and resources, best practices, and subject matter experts from other states will be addressed in this session. The presenters will discuss resources available from NCES and the State Support Team (SST) and how Alaska utilized and benefitted from those resources.

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II–J: Preventing Statewide Longitudinal Data System (SLDS) Errors Prior to Upload

Deborah Rodrigues, Pennsylvania Department of Education
Russ Redgate, eScholar LLC

    The Pennsylvania Department of Education (PDE) implemented new data quality capabilities in 2013–14. The Pennsylvania Information Management System (PIMS) Data Quality Engine (DQE) enables even some of the most complex business rules to be applied before data are allowed to enter the statewide longitudinal data system (SLDS). This session will explain how the state’s new DQE capability is increasing the quality of the SLDS data and reinforcing a culture of data quality within local education agencies (LEAs) statewide. In addition, the ability to prevent errors is saving time for local and state education agency staff. The PIMS DQE allows PDE to implement rules as simple as validating dates and numbers are valid, as well as more sophisticated rules requiring conditional logic that involves multiple fields or even multiple datasets. In some cases, the checks involve comparisons of incoming field values or record counts against data already residing in target tables. All this is possible prior to loading data into PIMS, PDE’s SLDS data warehouse.

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