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Upon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making.

This paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment.

Four main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions.

Three notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

The National Science Foundation (NSF) Research Experience for Undergraduates (REU) programs are traditionally delivered in-person and full-time (40 h per week) for 10 weeks during the summer. However, this type of format has the potential to limit broader student participation. This study aims to compare learning assessment data between a traditional NSF REU (10 weeks of summer, full-time, in-person) to an alternative NSF REU delivered virtually, part-time and over 10 months as a result of the coronavirus disease 2019 (COVID-19) pandemic.

A retrospective pre-then-post survey was completed to assess perceived learning gains for each REU program. Three learning gains categories were assessed: entrepreneurial competencies, career goals and research skill development. T-tests were used to evaluate a difference in means between pre and post.

Findings show the greatest quantity of learning gains within the alternative program delivery. Moreover, a larger quantity of learning gains was perceived within the first semester of the alternative program delivery compared to the second semester.

The authors propose the NSF should be intentional about trying new approaches to REU programs delivery, including duration and format, as a way to broaden participation in engineering.

This study is original in that it is the first of its kind to assess an alternative REU program delivery (allowed only because of the COVID-19 pandemic) in comparison to traditional REU program delivery.