|Title:||Understanding Pennsylvania's Educational Inequities in the time of COVID-19|
|Principal Investigator:||Hughes, Rosemary||Awardee:||Pennsylvania Department of Education|
|Program:||Using Longitudinal Data to Support State Education Recovery Policymaking [Program Details]|
|Award Period:||2 years (03/1/2021 – 02/28/2023)||Award Amount:||$998,574|
|Type:||Exploration and Efficacy||Award Number:||R305S210026|
Co-Principal Investigator: Lipscomb, Stephen
Partner Institutions: Pennsylvania Department of Education (PDE) and Mathematica
Purpose: The Pennsylvania Department of Education (PDE) and Mathematica will examine educational inequities in Pennsylvania that have occurred since the disruptions of COVID-19 and evaluate strategies that might reduce those inequities.
Project Activities: This work will address four issues:
Products: The research team will summarize findings in a report that will include strategies designed to mitigate the impacts of COVID-19 and recommendations to enhance the system of early warning indicators used in Pennsylvania. In addition, the project team will produce two briefs, two conference presentations, three academic papers, and four outreach sessions to disseminate findings more widely. The outreach sessions will target key staff at PDE, their 29 regional intermediate education service agencies, and local education agencies (LEAs).
Setting: The project will take place in Pennsylvania.
Sample: The sample will include all Pennsylvania local education agencies from 2010–2011 through 2021–2022.
Key Issue, Program, or Policy:COVID-19 and resulting, long-term school closures are likely to have major and long-term impacts on educational outcomes, especially among historically underserved student groups. This project will examine educational inequities in the wake of COVID-19 and assess whether strategies used to address the disruption in schooling caused by COVID-19 might reduce inequities and enhance student learning.
Research Design and Methods:The methods used will vary by issue.Researchers will use descriptive analyses to compare mean student outcomes (overall and by subgroup) in the 2020–2021 school year (SY) to previous years going back to SY 2010–2011. They will use quasi-experimental methods (propensity score matching and instrumental variables) to analyze how different school reopening and operating strategies are linked to education and health inequities.They will use descriptive analysis based on regression to explore how teacher characteristics (including experience, education, and match to students' race/ethnicity) are associated with student outcomes.Finally, the team will use predictive analytic methods and data on child welfare and juvenile justice to better identify students at risk of dropping out.
Control Condition: There is no true control condition, but the research team will examine variation in Local Education Agencies' responses to the disruption caused by COVID-19.
Key Measures:Student outcomes (achievement, attendance, enrollment, grade progression and high school graduation, credit accumulation, and disciplinary actions) and characteristics will be drawn from the Pennsylvania Information Management System (PIMS). Teacher outcomes (retention from 2019–2020 to 2020–2021 and inactivity during the 2020–2021 school year) and characteristics will be drawn from PIMS. Information on strategies used to address COVID-19 disruptions will be taken from information provided by LEAs to PDE, a survey of 200 LEAs regarding instruction during 2020–2021, and a student survey on instruction during 2020–2021 given by PDE.
Data Analytic Strategy: The research team will use descriptive analyses, regression analyses, and predictive analytic machine learning methods to analyze the data.
State Decision Making: The findings will be useful as PDE and the LEAs
Related IES Projects: Pennsylvania Information Management System (PIMS) (R372A060083); Strengthening PIMS Infrastructure to Expand Data Use Capacity (R372A200017); Mid-Atlantic Regional Education Laboratory