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Promoting Diversity in STEM Students for Higher Education Success

Explore how metrics and pathways analysis can support diversity in STEM student retention and success within higher education institutions. Learn about systematic measurement biases and ways to improve the analysis and reporting of retention data. Acknowledging the NSF grants that supported this work and the valuable contributions of colleagues in this field.

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Promoting Diversity in STEM Students for Higher Education Success

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  1. Matthew OhlandProfessor of Engineering EducationPurdue UniversityPromoting Diversity in STEM Students Susan Lord Russell Long Marisa Orr Cathy Brawner Xingyu Chen Richard Layton George Ricco STEP Community Webinar January 28, 2014, 1:00 PM Eastern Catherine Mobley Nichole Ramirez Michelle Camacho Mara Wasburn

  2. The Multiple-Institution Database for Investigating Engineering Longitudinal Development • 11 public universities, 1/9 of US engineering graduates • Predominantly southeastern, schools where 20% of enrollment is engineering (10% is average for schools with engineering) • Over 1 million unique students over a 20-year period including over 200,000 engineering students • Policy information available through catalogs, web archives, and consultation with partners

  3. Choose metrics that support diversity Intermediary measures (first-year retention, two-year retention) systematically overreport persistence of some populations. These figures show how persistence varies from eight semesters of enrollment to graduation within six years – how it varies by race, gender, and institution. There is also disciplinary variation.

  4. Pathways of White students

  5. Pathways of Asian students

  6. Pathways of Black students

  7. Pathways of Hispanicstudents

  8. Pathways of Native American students

  9. Choose metrics that support diversity • Intermediary persistence measures create a systematic majority measurement bias that overreports persistence of some populations. • How does your school analyze and report retention/attrition data? • Is it routinely disaggregated by race/ethnicity? • Is it routinely disaggregated by gender? • Is it ever disaggregated by both race/ethnicity and gender simultaneously

  10. Acknowledgements • Supported by National Science Foundation Grants 0337629/0729596, 0646441, 0734085/0734062, 0835914, 0935058, 0935157, 0969474, 1025171, 1129383 • Multiple graphs reproduced with permission • Conversations with various colleagues have certainly influenced our thinking on this work.

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