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This paper describes a National Science Foundation-funded Research Experiences for Undergraduates (REU) Site program conducted through virtual working environment. Due to the Covid-19 pandemic, REU 2021 activities were conducted online through Canvas and Zoom communication platforms. The major aim of this program is to provide undergraduate students with experiences in engineering education research (i.e., education research in the context of engineering). This paper provides an overview of the program, and briefly describes the virtual working environment, and students’ research experiences during the 10-week program. A total of 11 undergraduate students, seven graduate mentors, and seven faculty mentors have actively participated in the program. The program is conducted in two phases: Phases 1 (i.e., Weeks 1-2) and 2 (i.e., Weeks 3-10). Phase 1 consists of preparatory and foundational work that is delivered to participants and will allow them to begin Phase 2 with some educational research foundation already established. The results of the project evaluation show that the program has made a positive impact on increasing education research skills and communication skills of the participating REU students. The participating REU students reported that the research projects they worked on increased their motivation and confidence for continuing to engage in engineering education research. Four participants (i.e., 36.4% of the total participants) suggested that, if available, they would prefer face-to-face over a virtual REU program. Another four participants (i.e., 36.4%) felt that both face-to-face and virtual would offer the same quality of research experiences, and 3 participants (i.e., 27.2% of the total participants) voiced their preference of virtual over face-to-face REU program.
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Authentic Undergraduate Research in Machine Learning with The Informatics Skunkworks: A Strategy for Scalable Apprenticeship Applied to Materials Informatics Research
The Informatics Skunkworks program provides a new framework for engaging undergraduates in research experiences, with a focus on the interface of data science and materials science. The program seeks to provide authentic research, engaged personal learning, and professional development while also being efficient, accessible, and scalable. Initially developed at the University of Wisconsin-Madison, participation continues to grow, with over 90 students engaged in research or training activities during the Fall 2021 semester from 4 institutions. The Skunkworks focuses on reducing barriers to engagement for mentors and students in undergraduate research by replacing bespoke and ad-hoc approaches with efforts and infrastructure that are reusable and scalable, including simplified standardized recruiting methods, online modular training resources, flexible undergraduate accessible software tools, long-term research projects with many similar but distinct components to engage large teams, and support from a learning community. For example, new students have the option to participate in a modular, self-paced, online onboarding curriculum that teaches students the basic skills needed for most data science projects, thereby dramatically reducing the mentor time needed to engage students with limited background in machine learning research. Projects are authentic research challenges that strive to allow for large flexible teams, thereby scaling up their impact from the typical engagement of just one or two students and allowing for extensive peer teaching. Throughout the program, professional development activities are efficiently delivered through standardized materials to teach critical research skills like record keeping, establishing group expectations and dynamics, and networking. These skills are also reinforced at workshop events hosted during the semester, which are effectively delivered online and yield growing impact for modest effort as the community grows. The program has been successfully implemented as evidenced by the last two semesters' evaluation findings through interviews, focus groups, and pre-post surveys. The students reported a positive attitude towards the program. Students' perception about machine learning knowledge and skills and their self-confidence improved after they got involved in the program. The instructors and mentors indicated positive teaching and mentoring experiences, and shared ideas on the further improvement of the program. Building on its early successes the team is continuing to implement evaluation data-driven improvements to the program with the goal of continuing to grow through new collaborations.
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- Award ID(s):
- 2017072
- PAR ID:
- 10516064
- Publisher / Repository:
- ASEE Conferences
- Date Published:
- Journal Name:
- ASEE Annual Conference and Exposition,
- Format(s):
- Medium: X
- Location:
- Minneapolis, MN
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18260/1-2--40812
