STATS-DC

11–A Co-Designing an Evaluation of Virtual Virginia Outcomes, Implementation, and Costs

Jessica Mislevy, Regional Educational Laboratory Appalachia
Jennifer Piver-Renna, Virginia Department of Education
Jaunelle Pratt-Williams, Regional Educational Laboratory Appalachia

The Virginia Department of Education partnered with REL Appalachia @ SRI International to co-design and support an evaluation of Virtual Virginia, a state-funded program which offers online courses for middle and high school students. Presenters will share approaches developed through this work for collecting new sources of implementation and cost data, and integrating them with studies of a program's outcomes to inform decisions of policy and practice. Participants will learn ways to strengthen evaluation rigor using extant data, strategies to improve survey response rates, and methods to understand local cost that state education agencies can implement.

Complexity: Entry Level

11–B Student and School Characteristics Associated with Academic Performance and English Language Proficiency Among English Learner Students in Grades 3 - 8 in the Cleveland Metropolitan School District

Rachel Garrett, American Institutes for Research
Jose Gonzalez, Cleveland Metropolitan School District

The Cleveland Metropolitan School District (CSMD) has recently seen an increase in the size and diversity of its English learner student population. Regional Educational Laboratory Midwest collaborated with CSMD to examine the relationships between student and school characteristics with English learner student academic performance and English language proficiency. This included a focus on the relationships between school climate measures and English learner student performance. The findings can help CMSD determine where to focus its efforts and what relationships merit further investigation. The findings also may be relevant for other urban districts with growing English learner student populations.

Complexity: Entry Level

11–C EDFacts Data: A Technical Discussion for the Data Programmers

Jennifer Davies, U.S. Department of Education
Alexandra Henning, Quality Information Partners, Inc.
Laura Williams, Twenty Bridge

This session will be led by EDFacts data programmers and will include a quick overview of what EDFacts data is available publicly and how to access it. The major portion of the session will be an open forum discussion geared towards the researcher/data programmer. As the staff that prepare the EDFacts data products, we have a unique view of the data sources and would like to discuss how the data are utilized when they leave our hands. Possible discussions will include:

• Compatibility with other systems such as Python
• What are some of the tips you have for using the current file formats?
• How is privacy protection handled?
• What are the preferred apps to use?
• SAS example code for importing a file from CSV or Excel into SAS, printing data contents, comparing two file versions, merging two or more files, querying a table, and printing summary statistics
• Newbie to the data? What info can we provide?

Complexity: Entry Level

Download Download Zipped Adobe Presentation & Handout:

• EDFacts Data: A Technical Discussion for the Data Programmers (1.14 MB)

11–D Learning Time and Achievement: Evidence from a Nationwide Natural Experiment

John Klopfer, United States Naval Academy

This paper uses the National Assessment of Educational Progress (NAEP) to measure learning during the school year. I identify learning using random variation in assessment dates, within and across years of testing, in a nationally representative sample of 40,000 schools. Baseline learning in 4th grade is double that in 8th grade, when measured in test standard deviations: in math, 4th grade students gain 0.80 SD, and 8th grade students gain 0.40 SD, per additional year of instruction; in reading, 4th grade students gain 0.50 SD, and 8th grade students gain 0.25 SD. The estimates are precise (t=10 to 20), robust across specifications, and stable across years of NAEP testing. Question-level evidence shows that differences across grades and subjects are not due to response patterns, question difficulty, or reliability. Instead, learning rates decline from 4th to 8th grade partly because tests are cumulative (as in Cascio and Staiger, 2012), and partly because the taught curriculum is progressively less aligned with the tested curriculum.

Complexity: Intermediate Level

11–E A Data-Informed Statewide Framework for Improving Early Childhood Outcomes

Rebecca Planchard, North Carolina Department of Health and Human Services
Hayley Young, North Carolina Department of Health and Human Services

This presentation will provide attendees with guidance and lessons learned on developing the North Carolina Early Childhood Action Plan (NC ECAP), a statewide, data-informed framework to measurably improve outcomes for young children by 2025. The presentation will also provide strategies for aligning data systems, collection, and access to this type of statewide framework. NC ECAP prioritizes ten quantitative goals for children ages 0-8 that, when achieved, will provide all NC children a fair opportunity to grow up healthy in safe and nurturing families, schools and communities so that they are learning and ready to succeed.

Complexity: Intermediate Level

Download Zipped Adobe Presentation:

• A Data-Informed Statewide Framework for Improving Early Childhood Outcomes (3.41 MB)

11–F Data Security and Preparing for the Unexpected: How to Develop a Plan to Protect Your Organization

Ross Lemke, U.S. Department of Education
Mike Tassey, U.S. Department of Education

Understanding Data Security isn't all about understanding the present security threats. A strong security posture requires the development of comprehensive data security policies that effectively translate to useable procedures. Join the technical security experts from the Privacy Technical Assistance Center for this discussion of both ongoing threats and a how-to on developing a robust security policy.

Complexity: Intermediate Level

11–G Small State Doing Big Things - Vermont's Data System Plans and Preparation to Implement Generate

Samantha Steiner, Vermont Agency of Education
Bill Huennekens, Center for the Integration of IDEA Data (CIID)
Wendy Geller, Vermont Agency of Education

Attend this session to learn about the plans the Vermont Agency of Education has for its data systems and the preparation work under way for implementing Generate. Vermont staff will further outline their interest and why they are implementing Generate. CIID staff will participate to address questions and provide a brief update on the status of Generate development and implementations.

Complexity: Entry Level

Download Zipped PowerPoint Presentation:

• Small State Doing Big Things - Vermont's Data System Plans and Preparation to Implement Generate (1.91 MB)

11–H Accessing and Exploring NCES Data

Stephen Q. Cornman, U.S. Department of Education
Andrew White, U.S. Department of Education
Emmanuel Sikali, U.S. Department of Education
Douglas Geverdt, U.S. Department of Education

The National Center for Education Statistics (NCES) has several state-of-the-art data tools that allow users to easily access and analyze data. This session provides participants with a comprehensive overview of those tools to access data sets. Participants will learn how to access public-use and restricted-use data sets, create reports and data tables, find published reports and conduct analyses in selected statistical tools. NCES offers a large variety of national, state, local, school and student data sets including assessment data, cross sectional data, survey data and administrative records, and participants will better understand which data sets cover their educational topic of interest.

The workshop will introduce NCES' Distance Learning Dataset Training System (DLDT). The workshop will also offer an in-depth instruction on four NCES data tools, including the Elementary/Secondary Information System (ELSI); the National Assessment of Educational Progress (NAEP) Data Explorer; Educational Demographic and Geographic Estimates (EDGE) data tools to explore ACS and GIS data; and the new Integrated Postsecondary Education Data System (IPEDS).

Participants are not required to have any pre-requisite skills to attend and should bring their personal laptops for interactive activities. Each participant will receive an Accessing and Exploring NCES Data "cookbook," that provides comprehensive instruction and screen shots explaining how to navigate a myriad of NCES data tools and the DLDT.

Complexity: Intermediate Level

11–J Developing Effective Data Analytics from an SLDS/ECIDS: A Dynamic Stakeholder Engagement Approach for Analytics

Missy Coffey, ECDataWorks
Phil Sirinides, University of Pennsylvania
Howard Morrison, Texas Education Agency
Jennifer Verbrugge, Minnesota Department of Education

In this session the panel will share an evidence-based, dynamic stakeholder approach to developing data analytics from and SLDS and ECIDS. The model will be shared with participants with state experience on implementation from Minnesota and Texas. In addition, the evaluation of the model will be shared and participants will have an opportunity to interact with the model and discuss how variations may work for their states as effective analytics are developed.

Complexity: Intermediate Level

11–K Known Unknowns and Unknown Unknowns:  Managing Missing Data in SLDS Systems, Practical and Theoretical Considerations to Improving Data Quality

Sean Simone, Rutgers University, The State University of New Jersey

This session will provide an overview of challenges in managing and documenting missing data in SLDS systems using practical examples from New Jersey's P20W data system. The presentation will draw from the existing literature on survey methods using the Total Error Framework as a strategy for addressing the potential bias associated with missing population data. While specific strategies will be discussed, the presentation will present on methods used by survey statisticians that have potential to be appropriated by SLDS system owners to provide unbiased metrics.

Complexity: Intermediate Level