Search for: All records

Engineering education is typically described using a “pipeline” metaphor, wherein students are shuffled along pre-determined pathways toward a narrow set of career outcomes. However, several decades of research have shown that this pipeline model does not accurately describe engineering trajectories and may exclude students who enter the pipeline at different times and have other career outcomes in mind. Similarly, qualitative studies have shown that normative identities in engineering feature masculine stereotypes such as “geeks” and “nerds” that reproduce technical/social dichotomies. Several studies have suggested that broadening the expected outcomes and identities in engineering to include “alternative” pathways and identities may contribute to a shift to a more inclusive form of engineering education. To make these alternative pathways more visible to faculty and students, we have developed a set of engineering “personas” based on interviews [n=16] with senior engineering students at a liberal arts university. Interviews were coded by three members of the research team using consensus coding techniques to ascertain core elements of the personas: Origins, Identities, and Trajectories. Early drafts of student personas were presented to students, who provided insights into future iterations. We propose several engineering personas using a matrix approach, which allows each persona to be adaptable for various origins, identities, and trajectories. These personas contribute to our understanding of alternative engineering pathways based on real student experiences. We intend to use these personas as pedagogical tools to help faculty recognize a wider range of engineering identities, and to help students see themselves as “real engineers” without sacrificing other (non-technical) core values, identities, and pathways. 

The recent surge in artificial intelligence (AI) developments has been met with an increase in attention towards incorporating ethical engagement in machine learning discourse and development. This attention is noticeable within engineering education, where comprehensive ethics curricula are typically absent in engineering programs that train future engineers to develop AI technologies [1]. Artificial intelligence technologies operate as black boxes, presenting both developers and users with a certain level of obscurity concerning their decision-making processes and a diminished potential for negotiating with its outputs [2]. The implementation of collaborative and reflective learning has the potential to engage students with facets of ethical awareness that go along with algorithmic decision making – such as bias, security, transparency and other ethical and moral dilemmas. However, there are few studies that examine how students learn AI ethics in electrical and computer engineering courses. This paper explores the integration of STEMtelling, a pedagogical storytelling method/sensibility, into an undergraduate machine learning course. STEMtelling is a novel approach that invites participants (STEMtellers) to center their own interests and experiences through writing and sharing engineering stories (STEMtells) that are connected to course objectives. Employing a case study approach grounded in activity theory, we explore how students learn ethical awareness that is intrinsic to being an engineer. During the STEMtelling process, STEMtellers blur the boundaries between social and technical knowledge to place themselves at the center of knowledge production. In this WIP, we discuss algorithmic awareness, as one of the themes identified as a practice in developing ethical awareness of AI through STEMtelling. Findings from this study will be incorporated into the development of STEMtelling and address challenges of integrating ethics and the social perception of AI and machine learning courses. 

Traditional engineering curriculum and course structures prioritize preparing students for technical and logical reasoning skills that are intrinsic to becoming an engineer. While these skills are undeniably vital for an engineering career, these courses often fail to provide opportunities for students to explore skills that go beyond the traditional curriculum and classroom walls. In addition, course structures often reinforce the stereotypical narrative in engineering that there is a dichotomy between the social and technical aspects with the latter being more important. Preparing students for both social and technical sides of engineering, requires a reorganization of how learning environments are designed and how engineering programs and faculty evaluate how learning occurs.