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Data Use in the Design of Interventions to Improve Equity in Engineering Education | IEEE Conference Publication | IEEE Xplore

Data Use in the Design of Interventions to Improve Equity in Engineering Education


Abstract:

Introduction: This full research paper explores how data is used to design equity interventions. The percent of engineering degrees awarded to people who have historicall...Show More

Abstract:

Introduction: This full research paper explores how data is used to design equity interventions. The percent of engineering degrees awarded to people who have historically been excluded from engineering has increased since 2010. But there is still substantial underrepresentation for women and people from racially and ethnically marginalized groups. One potentially promising practice is to harness the power of data, including big data, to design interventions to improve equity. However, there are gaps in our understanding of how higher education faculty and staff use this data in designing equity interventions. Objective: The purpose of this paper is to answer the question: How is data used in the process of designing efforts to improve equity in engineering programs? Methods: We performed a content analysis on surveys completed by and artifacts generated by higher education faculty and staff who participated in a structured professional development and research experience. The experience focused on planning and executing a data-driven project designed to improve equity in engineering education. Results: Analysis of the data suggests the following: participants articulate the core challenge that they are facing in terms of data indicating demographic disparities, make the case for addressing inequities via presentation of relevant data, and conceptualize evaluating success via gathering quantitative data about intervention outcomes. However, many projects are not, themselves, focused on the creation or use of data (e.g., mentoring programs), Conclusion: This work shows how higher education faculty and staff who are designing interventions to promote equity in engineering education are using data in the design phase of their work. Understanding their patterns of data use is an important first step in determining how data use impacts their projects' outcomes and, based on that finding, how future cohorts can be best supported in the design of their interventions.
Date of Conference: 13-16 October 2024
Date Added to IEEE Xplore: 26 February 2025
ISBN Information:

ISSN Information:

Conference Location: Washington, DC, USA

Funding Agency:

References is not available for this document.

I. Introduction and Background

The percent of engineering degrees awarded to people who have historically been excluded from engineering has increased since 2010. But there is still substantial underrepresentation for women and people from racially and ethnically minoritized groups [1].

Select All
1.
"Diversity and STEM: Women Minorities and Persons with Disabilities 2023", N. C. for Science and Engineering Statistics, 2023.
2.
M. T. Hora, J. Bouwma-Gearhart and H. J. Park, "Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning", The Review of Higher Education, vol. 40, no. 3, pp. 391-426, 2017.
3.
A. Datnow and V. Park, "Opening or closing doors for students? Equity and data use in schools", Journal of Educational Change, vol. 19, no. 2, pp. 131-152, May 2018.
4.
T. Agasisti and A. J. Bowers, Data Analytics and Decision-Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions, pp. 184-210, 2017.
5.
K. K. Strunk and P. D. Hoover, "Quantitative Methods for Social Justice and Equity: Theoretical and Practical Considerations" in Research Methods for Social Justice and Equity in Education, Cham:Springer International Publishing, pp. 191-201, 2019.
6.
S. L. Dodman, K. Swalwell, E. K. DeMulder, J. L. View and S. M. Stribling, "Critical data-driven decision making: A conceptual model of data use for equity", Teaching and Teacher Education, vol. 99, pp. 103272, Mar. 2021.
7.
K. Hubbard, "Using Data-Driven Approaches to Address Systematic Awarding Gaps" in Doing Equity and Diversity for Success in Higher Education, Cham:Springer International Publishing, pp. 215-226, 2021.
8.
R. J. Delgado-Riley and L. Salazar-Montoya, "Equity-Centered and Data-Driven Decision-Making Leadership", Pursuing Equity and Suc-cess for Marginalized Educational Leaders. IGI Global, pp. 145-169, 2024.
9.
J. O. Love, C. Duggan and E. H. Blume, "stEm PEER Academy: the Power of Human Capital" in 2024 Collaborative Network for Engineering & Computing Diversity (CoNECD), 2024, [online] Available: https://peer.asee.org/stem-peer-academy-the-power-of-human-capital.
10.
J. O. Love, C. J. Duggan, J. A. Isaacs, J. M. Parker and K. M. Norris, stEm Peer Academy: Building a Community of Practice, Feb. 2023, [online] Available: https://peer.asee.org/stem-peer-academy-building-a-community-of-practice.
11.
J. O. Love, C. Duggan, J. Xavier, A. N. Slater and K. Rath, Engineering PLUS Alliance stEm PEER Academy for Faculty and Administrators: Transforming the National Engineering Education Landscape for Women and BIPOC Students, Jun. 2023, [online] Available: https://peer.asee.org/engineering-plus-alliance-stem-peer-academy-for-faculty-and-administrators-transforming-the-national/engineering-education-landscape-for-women-and-bipoc-students.
12.
E. Mandinach, M. Honey and D. Light, A Theoretical Framework for Data-Driven Decision Making, Jan. 2006.
13.
S. I. H. Shah, V. Peristeras and I. Magnisalis, "DaLiF: a data lifecycle framework for data-driven governments", Journal of Big Data, vol. 8, no. 1, pp. 89, Jun. 2021.
14.
I. S. Rubinstein and W. Hartzog, "Anonymization and risk", Wash. L. Rev., vol. 91, pp. 703, 2016.
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References

References is not available for this document.