Social Sciences: Innovation Studies, Engineering Education, Sustainability

Department: Engineering and Society, SEAS

Supervising Faculty Member: Rider Foley

Specialization: Innovation Studies, Engineering Education, Sustainability

Research Focus: To support the STEM educated workforce, universities and colleges across the nation integrate ethics into the curriculum in a variety of ways from stand-alone classes to short modules interspersed with technical training. However, little is known about how students acquire and thus, express ethics-based logic and reasoning in different institutional context. The 4C Project research explores the question: Does “one size fit all” STEM education programs? Four universities have joined together in a collaborative research effort to explore this research question and advance our understanding of the context within which ethics is introduced to STEM students.  To conduct this research, we need talented and engaged undergraduate research assistants to work with the lead researchers to gather, analyze, and interpret qualitative and quantitative data. This project will gather and analyze preliminary data.

Position Description: Students must be eligible to work at least 5 hours per week and per University policy, but may not work more than 10 hours during the academic semester.  The applicant must have a cumulative grade point average of at least 2.5 and be enrolled in at least 6 credits this semester.  The University of Virginia is an Equal Opportunity/Affirmative Action employer.  Work-study students are encouraged to apply.

Required skills: The student should be creative, detail-oriented, able to work independently and proficient in written and spoken English.  Skills in qualitative or quantitative data analysis is a plus, but not required.

Computer software: No computer software training required.

Training/certification: No prior training required.

What you will learn: The undergraduate research assistants to learn how to gather, analyze, and interpret qualitative and quantitative data.