Search for: All records

We explore the possibility of using natural language processing (NLP) and generative artificial intelligence (GAI) to streamline the process of thematic analysis (TA) for qualitative research. We followed traditional TA phases to demonstrate areas of alignment and discordance between (a) steps one might take with NLP and GAI and (b) traditional thematic analysis. Using a case study, we illustrate the application of this workflow to a real-world dataset. We start with processes involved in data analysis and translate those into analogous steps in a workflow that uses NLP and GAI. We then discuss the potential benefits and limitations of these NLP and GAI techniques, highlighting points of convergence and divergence with thematic analysis. Then, we highlight the importance of the central role of researchers during the process of NLP and GAI-assisted thematic analysis. Finally, we conclude with a discussion of the implications of this approach for qualitative research and suggestions for future work. Researchers who are interested in AI-assisted methods can benefit from the roadmap we provide in this study to understand the current landscape of NLP and GAI models for qualitative research. 

Graduate education in engineering is an extremely challenging, complex entity that is difficult to change. The purpose of this exploratory research paper was to investigate the applicability of the Collective Impact framework, which has been used within community organizing contexts, to organize the change efforts of a center focused on advancing equitable graduate education within engineering. We sought to understand how the conditions of Collective Impact (i.e., common agenda, backbone organization, mutually reinforcing activities, shared measurement system, and continuous communication) could facilitate the organization of equity-focused change efforts across a college of engineering at a single institution. To achieve this, we took an action research approach. We found the Collective Impact framework to be a useful tool for organizing cross-sectional partnerships to facilitate equity-focused change in graduate education; we also found the five conditions of Collective Impact to be applicable to the higher education context, with some intentional considerations and modifications. Through coordinated efforts, the Collective Impact framework can support the goal of reorienting existing decentralized structures, resource flows, and decision processes to foster bottom-up and top-down change processes to advance equitable support for graduate students. 

This Work-in-Progress paper studies the mental models of engineering faculty regarding assessment, focusing on their use of metaphors. Assessments are crucial components in courses as they serve various purposes in the learning and teaching process, such as gauging student learning, evaluating instructors and course design, and documenting learning for accountability. Thus, when it comes to faculty development on teaching, assessments should consistently be considered while discussing pedagogical improvements. To contribute to faculty development research, our study illuminates several metaphors engineering faculty use to discuss assessment concepts and knowledge. This paper helps to answer the research question: which metaphors do faculty use when talking about assessment in their classrooms? Through interviews grounded in mental model theory, six metaphors emerged: (1) cooking, (2) playing golf, (3) driving a car, (4) coaching football, (5) blood tests, (6) and generically playing a sport or an instrument. Two important takeaways stemmed from the analysis. First, these metaphors were experiences commonly portrayed in the culture in which the study took place. This is important to note for someone working in faculty development as these metaphors may create communication challenges. Second, the mental model approach showed potential in eliciting ways engineering faculty describe and discuss assessments, offering opportunities for future research and practice in faculty development. The lightning talk will present further details on the findings. 

{} 

In this Research Full Paper we explore the factors that traditionally minoritized students consider when selecting a graduate school to pursue a doctoral degree in an engineering discipline. To this end, we used case study methods to analyze the experiences of ten traditionally minoritized students through interviews conducted immediately after they had selected their graduate programs, but before they had commenced their studies. Our findings show that in choosing an institution, the most salient ideals these students hold are related to the offer of funding towards their degree and an alignment with their initial research interests. However, they described having made compromises on ideals related to their personal experience and racial identity, the most prominent being finding a faculty mentor with a similar racial background, finding a racially diverse institution, or being located in a geographical location they perceived to be more amenable to their individual identities. These findings suggest that continuing to increase the recruitment of traditionally minoritized faculty in engineering schools would have a direct impact on minoritized student recruitment, by thus helping to create spaces where more of their racial identity ideals are met and fewer compromises are made. Equally important to the recruitment of traditionally minoritized students is the transparency of funding opportunities during the recruitment and application processes, and the publication of current research opportunities within the institution. 

This paper proposes the use of collaborative secondary data analysis (SDA) as a tool for building capacity in engineering education research. We first characterise the value of collaborative SDA as a tool to help emerging researchers develop skills in qualitative data analysis. We then describe an ongoing collaboration that involves a series of workshops as well as two pilot projects that seek to develop and test frameworks and practices for SDA in engineering education research. We identify emerging benefits and practical challenges associated with implementing SDA as a capacity building tool, and conclude with a discussion of future work.