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.
Download Roundtable Discussion Notes:
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.
Download Zipped PowerPoint Presentation:
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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