Not just data: why data equity matters in education program evaluation
Abstract/Contents
- Abstract
- In program evaluation, evaluators make hundreds of decisions that impact millions of students who participate in educational programs outside of school every year. These decisions often reflect personal and structural biases, but centering data equity throughout the evaluation process can guide evaluators to make more equitable decisions. This study proposes a framework — Data Equity in Education Program Evaluation (DEEPE) — for embedding data equity decisions throughout the evaluation process. The framework includes three stages, each with a focal principle: (1) planning: center racism and the voices of marginalized students, (2) analysis: challenge categorization, and (3) recommendations: use asset-based narratives. I use a case study of an online upskilling and reskilling program to demonstrate how to apply DEEPE and how it compares to a “traditional” evaluation. The evaluations focus on students’ course and certificate completion rates in the program. The resulting insights show that centering data equity can lead to program evaluations that are more accurate, highlight deeper inequities, and push for integrating equity in future steps. As education continues to become increasingly data-driven, embedding data equity into evaluation processes will be critical to achieving educational equity.
Description
Type of resource | text |
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Date modified | March 30, 2023 |
Publication date | March 30, 2023 |
Creators/Contributors
Author | Hinton, Laura |
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Thesis advisor | Pearman, Francis A. |
Department | Graduate School of Education |
Degree granting institution | Stanford University |
Subjects
Subject | Data equity |
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Subject | Educational evaluation |
Subject | Data science |
Genre | Text |
Genre | Capstone |
Genre | Student project report |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Hinton, L. (2023). Not just data: why data equity matters in education program evaluation. Stanford Digital Repository. Available at https://purl.stanford.edu/zh004rv6465. https://doi.org/10.25740/zh004rv6465.
Collection
Education Data Science (EDS) Capstone Projects, Graduate School of Education
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- Contact
- lshinton@alumni.stanford.edu
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