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CUE4CHNG

Coordinating Curricula and User Preferences to Increase the Participation of Women and Students of Color in Engineering

For several decades, academic institutions have received financial resources to broaden participation in engineering programs. Despite these funded recruitment and retention efforts, most engineering programs have achieved little improvement in the participation of women, students of color, individuals with disabilities, and other underrepresented groups. One hypothesis to explain this discrepancy is that the low representation of women and students of color in engineering results from the lack of accessibility of engineering curricula. To test this hypothesis, this project seeks to study engineering curricula and student preferences. Based on those results, it aims to develop a set of curriculum guidelines and models that may increase the alignment between engineering curricula and students' expectations and preferences for learning. These results have the potential to broaden participation of women and students of color in engineering.

The project uses an exploratory sequential mixed methods design, which begins with exploratory activities and builds to more systematic testing of research questions concerning how particular user preferences influence student participation. Investigators will engage in data mining of syllabi, course content, public spaces, and instructor materials to aggregate information about curricula presented in different representational forms, such as equations, images, narratives, simulations, and videos. From these data, it is anticipated that qualitatively and quantitatively distinct profiles of course representations will be generated. The investigators will track longitudinal persistence of students in STEM, in relationship to course structure and individual learner preferences. Understanding the intersection between learner preferences and engineering curricula has the potential to improve engineering education and broaden participation in the field of engineering.

Leadership Team

Sharon Tettegah, University of Santa Barbara (leading PI)
Yingtao Jiang, University of Nevada, Las Vegas (Co-PI)
Sandra Gesing, University of Notre Dame (Co-PI)
Rafael Ferreira da Silva, University of Southern California, Los Angeles (Co-PI)

This project is being supported by the National Science Foundation in the Division Of Undergraduate Education in the Directorate for Education and Human Resources (EHR) Under Grant 1826632.

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  • Copyright © 2019 CUE4CHNG
  • This project is being supported by the National Science Foundation in the Division Of Undergraduate Education in the Directorate for Education and Human Resources (EHR) Under Grant #1826632.