Project Activities
This research team will use quantitative analyses of administrative and survey data, qualitative analyses of case study data, and quantitative analyses of student-level longitudinal data to examine (1) how ELO grant funding is used to promote educational recovery from COVID-19, (2) how ELO funding is targeted to address educational inequities across student groups, and the (3) institutional factors that shape ELO grant implementation across and within districts. The research team will examine the correlation of these factors with changes and levels of observable school inputs, demographics, and student outcomes to understand the potential impacts of state-funded recovery activities. Weekly partnership meetings will ensure that the findings are accessible, actionable, and relevant to the needs of the California Department of Education (CDE). The partnership will provide training, and mentorship to CDE on how to connect research to ongoing recovery work.
Structured Abstract
Setting
This project will take place in California.
Sample
The primary sample covers the universe of public K-12 school districts in California, collectively serving approximately six million school children, from 2021-22 through 2024-25. Sixty percent of California public K-12 students are free/reduced price lunch eligible, over half are Latino, and nearly one-fifth are current English Language Learners. Charter schools will be excluded. Case studies of 8 to 10 individual school districts will provide in-depth information from districts selected to vary by size, region, geography, and student need.
The primary factors under investigation include (1) how ELO grant funding was used to promote educational recovery from COVID-19, (2) how ELO funding was targeted to address educational inequities across student groups, (3) institutional factors that shape ELO grant implementation across and within districts. The research team will then consider the correlation of these factors with changes and levels of observable school inputs, demographics, and student outcomes to understand the potential impacts of state-funded recovery activities.
Research design and methods
The research will use a mixed methods approach combining quantitative analyses of administrative and survey data with qualitative case study data. Text-based information from posted ELO plans and expenditure reports will be collected annually to identify district recovery actions and expenditures. Annual surveys of school districts will provide information on district recovery actions. Regression methods will document the association between district actions and observable district-level changes in inputs and student outcomes. In-depth case studies will provide information on the institutional forces shaping the implementation of grant funding within districts, informing both the annual surveys and the interpretation of the quantitative findings.
Control condition
The research will primarily be descriptive and there is no control condition. Associations with intermediate district and student outcomes will involve comparisons across specific district actions, by the magnitude of ELO funding, and by differences in the timing and grade levels affected by ELO funded activities.
Key measures
The research team will identify and classify specific district actions taken to accelerate student learning (e.g., high dosage tutoring; extending learning time) from district ELO planning documents. The team will use planned and actual expenditures to classify the magnitude of the specific district actions and drawn from district expenditure reports. They will gather institutional factors (e.g., degree of centralization in district decision-making, institutionalized habits and expectations, and capacity of district leaders to collaborate with school-level leaders) through case study interviews. The team will include district characteristics (e.g., geography, demographics), district intermediate outcomes (e.g., inputs such as staffing and high school course offerings), and student outcomes (e.g., math and ELA test scores, college preparatory curriculum completion, high school graduation) from state administrative data.
Data analytic strategy
The research team will use natural language processing and machine learning methods to identify the specific district recovery actions from district ELO plans and expenditure reports. In addition, district recovery actions will be identified through the analysis of the annual surveys of school districts. Descriptive regression, differences-in-differences, instrumental variables, and regression kinked design methods will identify associations between ELO-funded recovery activities, changes in district inputs, and student outcomes. Potential non-randomness in selection of specific actions will be reduced by including observable control variables and by examining within-district and predicted funding changes over time. Case studies findings will provide context on activities and institutional factors, helping to refine subsequent surveys and data analyses and to inform policymakers about the implementation of ELO grants for educational recovery from COVID-19 in California.
People and institutions involved
IES program contact(s)
Project contributors
Partner institutions
California Department of Education
Products and publications
Products: The partnership will provide the findings in multiple formats for a set of audiences including the California Department of Education (CDE), the CA State Board of Education (SBE), local school districts, other CA policymakers, professional education organizations, community-based organizations and advocacy organizations, and the general public. The formats will include infographics, dashboards, articles, posts, practitioner and policymaker briefs, 2-3 PPIC reports and one summative PPIC report, three journal submissions, workshops and webinars, small group briefings, public briefings, and presentations
Additional project information
Previous award details:
Supplemental information
Co-Principal Investigators: Betts, Julian; Fuller, Bruce; Hill, Laura; Isler, Jonathan; Lafortune, Julien
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
