Concurrent Session X Presentations
Friday, August 1, 2014
9:00 am – 10:00 am
X–A: Creating Rich School District Datasets
Through Updated Surveying Methods
Kati Stratos and David Kowalski, School District of Philadelphia (Pennsylvania)
In years past, the School District of Philadelphia (SDP) has administered an annual districtwide
parent and student survey using a paper-and-pencil-based system that was inconsistent and
unreliable and that yielded low response rates. In 2013–14, the surveys were moved entirely
online and now require a unique student ID in order for a parent or student to respond. This
session will discuss these changes and how they have allowed existing family demographic and
student performance data to be triangulated with survey response data on the back end, creating
a more robust, high-quality dataset to inform school improvement efforts, program evaluation,
and district decisionmaking.
X–B: DC’s At-Risk Funding and Implications for
Early Warning Systems (CANCELLED)
Alex Engler, District of Columbia Office of the State Superintendent of Education
In 2014–15, Washington, DC, will be allocating to schools an additional $2,079 for each student
deemed “at-risk.” Learn about the factors currently used to determine risk for funding, the use
of early warning system indicators to drive risk evaluation, and the corresponding efforts to
better understand the impacts of poverty and socioeconomic status (SES) in a state that has
fully embraced the community eligibility provision to expand access to free school lunch. This
presentation will include analysis utilizing new indicators of SES and risk and linking them to such
outcomes as attendance, DC CAS scores, SAT scores, discipline, and school disengagement.
X–C: Using Data to Identify Indicators of College Readiness
Vasuki Rethinam, Howard County Public School System (Maryland)
A large number of students are entering college underprepared for rigorous college-level work,
leading to remedial course taking, delays in graduation, and dropping out of college. How can high schools help prepare students for college and career success? This session will demonstrate
research on developing a statistical model to identify indicators of college readiness and fall
college enrollment. This session will also highlight the strategies that a district and its schools use
to leverage indicator data to promote students’ enrollment in rigorous coursework.
X–D: Effective Strategies for Granting Statewide Longitudinal
Data System (SLDS) Access to Outside Researchers
Erica Orians, Utah Education Policy Center
Utah’s state longitudinal data system (SLDS), the Utah Data Alliance (UDA), offers researchers
outside the partner agencies the opportunity to use SLDS data in a secure environment for
research purposes. In order to extend these research opportunities to graduate students, faculty,
and other researchers, the Utah Data Alliance has developed and implemented extensive policies
and practices related to data access and security. This session will discuss the application process
for outside researchers, procedures to secure data and access, researcher support, usage and
issue tracking, and other protocols that could be adopted by other states.
Download Zipped PowerPoint Presentation:
X–E: Teacher and Leader Evaluation Management System
Irene Koffink and Ginny Clifford, New Hampshire Department of Education
Dean Hupp, Hupp Information Technologies
New Hampshire has been working on a data system to manage the evaluation of educators (both
teachers and leaders). The system allows superintendents and principals to track evaluations,
document evaluations in progress, capture professional development needs, track deadlines,
and include teacher responses to the evaluation. The data system is flexible so that each school
district can define an evaluation rubric (e.g., using the Charlotte Danielson or Marzano structure),
identify and document “indicators of effectiveness” used as input for the evaluation, and allow
for documents to capture classroom observations, student surveys, or peer feedback. The system
captures all of this information, which is then used to complete an evaluation rubric. Join us for a
demonstration of the pilot tool that New Hampshire schools will be using this fall. The tool is being
developed by Hupp Information Technologies, and a representative from Hupp will participate in
the presentation.
X–F: The Future Is Here in Massachusetts With Actionable, Near-Real-Time,
Event-Driven Data Integration Using Schools Interoperability Framework (SIF)
William Holscher, Massachusetts Executive Office of Education (EOE)
The Massachusetts Executive Office of Education (EOE) delivered a near-real-time, event-driven
data management and reporting system utilizing the School Interoperability Framework (SIF). The state is replacing legacy periodic data collection and reporting with a new event-driven model that
delivers actionable information to key stakeholders where and when needed. In this session, the
Massachusetts SIF Program Manager will share the implementation strategy, tactical approach,
and lessons learned while reviewing valuable tools and resources available to other states.
Download Zipped PowerPoint Presentation:
X–G: Civil Rights Data 2014: Use Cases and Stakeholder Stories
Melanie McCalmont, Wisconsin Department of Public Instruction
Abby Potts, U.S. Department of Education
Marlene Dorenkamp, Iowa Department of Education
Who uses the Civil Rights Data Collection (CRDC)? What decisions can it inform? This panel will
tell stories from those who have used CRDC data in many fields of inquiry and discuss the benefits
and limitations of its scope. Learn more about how the Office for Civil Rights and state education
agencies are partnering to improve data quality that will make data inquiry, policy analysis, and
school improvement more effective. An update of the upcoming 2014 CRDC survey will also be
provided.
Download Zipped PowerPoint Presentation:
X–H: Distance Learning Dataset Training System: An Online
Guide to NCES Data Across the Education Spectrum
Andrew White, National Center for Education Statistics
Jennifer Nielsen, Manhattan Strategy Group
The Distance Learning Dataset Training (DLDT) System is a new approach to facilitating the use
of education data. This session on the DLDT common modules offers an overview of NCES data
systems; methods used to ensure consistent, reliable, complete, and accurate data collection and
reporting; complex sampling designs and implications for micro-data users; and how to access
publications, products, data tools, and public- and restricted-use datasets. Survey-specific modules
present more detailed information about datasets from selected studies conducted by NCES.
Currently, five sets of modules detailing nine NCES surveys are available. Module sets detailing
additional survey and administrative datasets will be added annually.
Download Zipped PowerPoint Presentation:
X–I: Collaboration, Convergence, and Cost Containment—Using
Common Education Data Standards (CEDS) and Ed-Fi to Drive
Down Total Cost of Ownership While Accelerating Innovation and
Delivering Longitudinal Data Solutions
Patrick Bush, Delaware Department of Education
Christina Kucek, Pennsylvania Department of Education
Dean Folkers, Nebraska Department of Education
Troy Wheeler, Ed-Fi Alliance
This session will provide three state stories capitalizing on the convergence of Common Education
Data Standards (CEDS) and how the adoption of the Ed-Fi technology suite speeds solution
delivery, creates a conduit for state education agency collaboration, leverages interstate sharing,
and promotes accelerated data conversations in a common language that wasn’t possible before.
X–J: Disclosure Avoidance in Public Reporting: The Good, the Bad, and the Ugly!
Michael Hawes, U.S. Department of Education
Using examples from various state education agency websites, this session will provide an
overview of a number of disclosure avoidance methods commonly used to protect privacy in
public reporting of tabular data, and some common pitfalls and mistakes to avoid.
Download Zipped PowerPoint Presentation and Handout:
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