Report: Overreliance on Aggregrated Student Data Contributes to Equity Barriers
When institutions rely on aggregated data to develop practices and policies for all "underrepresented" or "disadvantaged" students, equity suffers, according to a new report from Every Learner Everywhere (ELE). While aggregated student data is sometimes necessary to initiate analysis and discussion, the report said, these monolithic categories fail to account for the nuanced ways that students' unique racial, ethnic, and economic backgrounds impact admissions, course-level outcomes, persistence, graduation, and career success.
In "Toward Ending the Monolithic View of 'Underrepresented Students:' Why Higher Education Must Account for Racial, Ethnic, and Economic Variations in Barriers to Equity," ELE "synthesizes commentary, research, and programmatic activity on how higher education has so far grappled with disaggregating and using student data to confront and close equity gaps for particular student populations," the organization explained in a news announcement. The findings were informed by interviews with faculty, administrators, researchers, advocates, students, and other experts.
Based on a scan of current literature on the topic, the report identifies a number of ways that aggregated data is problematic:
- While reports may characterize their data as disaggregated by race or ethnicity, many are sorting data by white vs. Black students, white vs. all minority students, or white vs. Black vs. "other" minority students — overlooking the diversity of other ethnic groups such as Hispanic, Asian American, or Indigneous students.
- Many reports about higher education focus primarily on four-year colleges. By not including associate or certificate programs or two-year colleges, they leave out significant populations of minority, poverty-affected, and first-generation students.
- Many studies collect data on admissions, degree attainment, and employment outcomes, yet data on teaching practices — and particularly disaggregated data about course-level or program-level learning outcomes — is sparse.
In contrast, the report points out, disaggregated data offers an array of potential benefits:
- Understanding the differences within groups, and elevating the voices and experiences of individual students, helps identify what is and isn't effective to support specific groups of students.
- Faculty can refine their teaching practices and develop data-informed partnerships across their institutions.
- Institutions can better understand systemic inequities and address their impact on students.
The full report includes an extensive analysis of how accounting for variations in student populations can better support equitable practices, examples of how institutions are removing barriers to equity, and additional resources for data-informed, equity-centered digital learning. It's freely available for download on the Every Learner Everywhere site.
About the Author
Rhea Kelly is editor in chief for Campus Technology, THE Journal, and Spaces4Learning. She can be reached at [email protected].