Collaborative Research: The Redshirt in Engineering Consortium

点击翻译按钮获取中文摘要

Low-income students are underrepresented in engineering and more likely to struggle in engineering programs. Research has found that increasing first and second-year retention enhances the ability of academically talented low-income students to successfully graduate with engineering degrees. In this collaborative research project six institutions will replicate, improve, and test a model of student success originally developed at the University of Colorado, Boulder. The model is designed to increase the retention, success, and graduation of low-income (Pell-eligible) academically talented students from underserved populations. The project will make scholarship awards to 800 students across a consortium of the six partner institutions. To support the students, the project will adapt and implement an ecosystem of high quality evidence-based curricular and co-curricular activities. Members of the consortium are: the University of Colorado, Boulder; the University of Washington; Washington State University; Boise State University; the University of Illinois, Urbana-Champaign; and the University of California, San Diego. The Redshirt in Engineering Consortium is committed to propagating the "Redshirt" model, which focuses primarily on the first-year of college and consists of intrusive academic advising, an innovative first-year academic curriculum, community building, and career awareness. The term "redshirt" refers to the idea of providing an extra year of preparation for the rigors of engineering curricula. A quantitative and qualitative mixed methods research study will examine the implementation of the model under different conditions and with different student populations. A comparative longitudinal study will examine differences in expected student outcomes between scholarship recipients and similar students who are pursuing engineering degrees. The primary analytic approach for the ethnographic research will be the Constant Comparative Analysis, which will involve concurrent engagement in data collection and data analysis.