91涩漫

The 91涩漫-ICPSR Partnership for Expanding Education Research in STEM (PEERS) Data Hub STEM Data and Data Use Series, features workshops that examine the structure, design, and purpose of multiple data sets that can be analyzed using quantitative, qualitative, and/or multi-method techniques. In these workshops participants can explore diverse forms of designed data, administrative data, and big data that can be used in STEM research. The publicly available datasets presented include large-scale student and school data across educational levels and contexts, such as federally supported datasets that provide a national scope as well as data that focus on specific states, regions, and local areas. Many of the data sets can be analyzed using custom designed analysis tools that will be presented in the webinars.

Led by experts in education research, psychometrics, and the social sciences, the workshops provide an overview of the data sets and discuss potential uses to address salient topics in education and STEM research. These workshops are ideal for graduate students, early career and senior researchers that are looking for large scale datasets to answer STEM related research questions. A description of each workshop is below. Each workshop is 1-1.5 hour long and the recordings are available with supporting documents in the and/or on 91涩漫 YouTube channel.

Workshop Descriptions - PEERS STEM Data and Data Use Series

Unlocking the STEM Education Data Vault
Date recorded: Thursday, June 11, 2026
Speakers: Lynette Hoelter, ICPSR, Julia Kaufman, RAND Corporation, and Sean Simone Pell Institute
Description: In this webinar, attendees were introduced to three important sources of STEM data and the online analysis tools offered by these sources. Learn about the breadth and depth of STEM education related datasets available for research from experts.  In this webinar, attendees will:

• Gain an overview of the variety of STEM related datasets available through ICPSR, RAND Corporation and Federal agencies
• Learn the requirements to access and use the data in their own research
• Receive an introduction to NCES’ DATALAB and POWERSTAT tools for conducting analysis

Learning Opportunities Early and Often: Longitudinal Findings From the Study of Early Child Care and Youth Development (SECCYD)
Date recorded: July 16, 2026 
Speakers: Andres Bustamante, University of California, Deborah Vandell, University of California, and Eric Dearing, Boston College
Description: Longitudinal datasets like the Study of Early Child Care and Youth Development (SECCYD) are uniquely powerful tools for educational and child development researchers. With 26 years of data, the SECCYD follows a large cohort of children from birth with rich assessments of their development – in cognitive, social, emotional, and relational domains – and the quality of contexts in which they grow, at home, in early education, in school, in after school, and in their communities. In this presentation, Drs. Andres Bustamante, Eric Dearing, and Deborah Vandell will use their multiyear collaboration to illustrate how data from the SECCYD can be leveraged to uncover new insights on the impact of developmental and educational opportunities across childhood and adolescence. In this talk, the authors will discuss their work, the history of their collaboration, and practical steps toward accessing and using the SECCYD. 

Unleash the Power of SLDS: Preschool through grade 20 Datasets for STEM Research
Date recorded: July 23, 2026
Speakers: Beth Kelly, KYSTATS, Kentucky, Rachel Leventhal-Weiner, DataLinkCT, Connecticut, and Tod Massa, VLDS, Virginia
Moderator: Dr. Nancy Sharkey
Description: Many states have combined administrative datasets to create a system that can track a student's experience from early learning through postsecondary school and into the workforce.  These databases are often referred to as State Longitudinal Data Systems (SLDS) or P20W (Preschool through grade 20) systems and can transform educational research.  They can include data from K-12, Postsecondary, Adult Education, workforce, benefit program participation and sometimes law enforcement systems.  Experts from Virginia, Kentucky and Connecticut discuss key aspects of their SLDS systems including:

• The types of data available in the system (e.g., K-12, post-secondary, program participation, etc.)
• Where to find documentation on using the system and variable names
• Requirements for researchers to access the system and privacy protections
• Dashboards and publicly available access tools

Studying Math Pathways with Higher Education State Administrative Data: Opportunities and Challenges
Date recorded: August 13, 2026 
Speakers: Yuxin Lin, Maryland Higher Education Commission and Florence Xiaotao Ran, University of Delaware
Description: State administrative data sets are a powerful tool for longitudinal analysis.  This workshop will introduce graduate students to a novel analysis of Maryland Higher Education Commission (MHEC) data by Drs. Lin and Ran that looked at the impact of math course taking requirement policy reforms in community colleges and its impact on choosing a STEM major. While this study focuses on the results using Maryland community college data, the presenters will also discuss the availability and potential use of data from four-year universities within the state. As Maryland's state higher education agency, MHEC maintains statewide administrative student unit-record data that capture enrollment, course-taking, financial aid, and degree completion across the state's public and independent institutions, including all 16 community colleges, 13 public four-year institutions, and 13 independent private colleges and universities.  Attendees will gain an understanding of:

• The Maryland Higher Education Dataset, requirements to access the data and available dashboards
• How Drs. Lin and Ran collaborated on the project
• How they selected variables and analysis techniques for their research and disseminated results

Using Large-Scale Data for STEM Education Research: Lessons from the High School Longitudinal Study of 2009 (HSLS:09)
Date recorded: August 18, 2026 
Speaker: Guan Saw, Claremont Graduate University
Description: This workshop introduces graduate students and early-career researchers to using the High School Longitudinal Study of 2009 (HSLS:09) for STEM education research. Dr. Guan Saw presents his work on rural STEM opportunities and pathways as a case example, discussing the use of publicly available and restricted-use HSLS:09 data, linked with the Common Core of Data (CCD).  Attendees learn how to:

• Identify promising research questions
• Select relevant variables
• Employ appropriate analytic techniques

Using Machine Learning to Analyze SLDS Datasets
Date recorded: August 20, 2026
Speaker: Tracy Sweet, University of Maryland
Description: This workshop introduces researchers to the use of machine learning techniques to analyze a State Longitudinal Data System (SLDS).  Dr. Tracy Sweet discusses her recent research applying machine learning algorithms to the Maryland Longitudinal Data System (MLDS) to predict student outcomes for education research.

Machine learning (ML) is a set of algorithms used for prediction. In one study, Dr. Sweet and her colleagues used supervised ML algorithms to look at the impact of taking 15 credits during the first year of college on returning in the second year and attaining a college degree within 5 years.  In other studies, they attempted to predict Algebra 1 PARCC scores and graduate school enrolment using MLDS data. 

In this workshop attendees learn about:

• Accessing and using the MLDS dataset
• Common supervised Machine Learning algorithms and evaluation metrics
• Interpreting ML results and understanding the potential for bias in these models

View the STEM Data and Data Use Workshops in the

The PEERS Data Hub is joint effort of 91涩漫 and the Inter-University Consortium for Political and Social Research (ICPSR) that is NSF funded (Award ECR 1937612). It provides collaborative space for STEM education research communities to build and advance knowledge by sharing innovative ideas, methods, and tools. Further information about the PEERS Data Hub is available .