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IES Grant

Title: Understanding Pennsylvania's Educational Inequities in the time of COVID-19
Center: NCER Year: 2021
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:

  • How COVID-19 disruptions of school operations exacerbate inequities in student outcomes.
  • How different strategies used to address the disruptions caused by COVID-19 affect educational outcomes, health outcomes, and inequities in both.
  • Whether teacher characteristics are related to differences in student outcomes or to different effects of responses to the pandemic.
  • Whether using data from outside of the education system can help offset the reduced reliability and validity of school attendance and behavior data caused by the COVID-19 disruptions and maintain the early warning system used to identify students at risk of dropping out.

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).

Structured Abstract

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 20202021 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

  • Identify which students are most impacted by COVID-19 and consider interventions to support such students,
  • Refine the strategies to deal with any future long-term school closures,
  • Identify remote-learning strategies that are likely to be useful in many different contexts even in the absence of school closures,
  • Consider recruitment and retention strategies for the kinds of teachers who may best serve students who were most harmed by the COVID-19 disruptions, and
  • Improve early warning measures used in the state's system of continuous school improvement.

Related IES Projects: Pennsylvania Information Management System (PIMS) (R372A060083); Strengthening PIMS Infrastructure to Expand Data Use Capacity (R372A200017); Mid-Atlantic Regional Education Laboratory