This research full paper presents a longitudinal multiple-methods project focused on the phenomenon of graduate attrition from the engineering PhD. There are relatively few investigations of graduate student attrition, and even fewer collect longitudinal and nationwide data representing dominant graduate student experiences. To this end, over the past several years, we have collected interview and longitudinal survey data from several large nationwide data collection efforts capturing data current students who are questioning leaving their PhD, many of whom decided to leave either with no degree or leaving with a Master's degree instead of a PhD. From these data, this paper investigates one critical aspect of attrition and persistence in doctoral education: the “advisor relationship.” To this end, this paper answers the overarching research question: How do “questioning” doctoral engineering students' perceptions of their research advisors interact with other factors to promote attrition and persistence, and how do perceptions of relationships change over time? The data analyzed in this study includes nationwide interview data with$\mathrm{N}=41$participants, and a longitudinal study of$\mathrm{N}=113$doctoral engineering students who are considering whether to persist or depart. Data are analyzed using methods relatively new to engineering education research: Qualitative analysis was conducted using Qualitative Comparative Analysis (QCA), a method that quantifies qualitative data to understand the“ causal configurations” of factors that lead to an outcome: In this case, questioning departure. We also employ a novel longitudinal SMS survey study, showing data collected over several years. Implications offer new perspectives to advisors, graduate chairs, and academic administrators who interface with graduate student issues, to work toward a more supportive environment in which graduate students can thrive. We show that advisor relationship is of significant importance, and that it is important to continue to attend to advisor relationship especially for late-stage graduate students.

This work in progress presents current findings from a funded mixed-methods investigation of the relationship between data proficiency and engineering identity among undergraduate students throughout their curriculum. This study aims to understand ways engineering undergraduate students conceptualize data proficiency and develop data skills over time. Through semi-structured interviews with four undergraduate engineering students from different class levels, we examined their understanding of data proficiency and the importance of data skills in engineering practice. The interviews were guided by the How People Learn framework, which provided a lens through which to investigate students' attitudes, beliefs, and experiences related to data and data analysis. The findings suggest that students view data proficiency as an important skill for their future careers but differ in their preferences for learning data skills through assignments, projects, or lectures. This research contributes to the understanding of how engineering students define and develop data proficiency, which can inform the design of effective data skills curricula in engineering education.

Postdoctoral training increasingly represents an essential step in advanced research careers in engineering and computer science; however, little is known about postdoctoral mentorship practices, competencies, and needs. The scales evaluated here present an opportunity to investigate global and specific aspects of mentorship competency in postdoctoral engineering and computer science training and education. The global construct of mentorship measured may help assess overall mentor capabilities for postdoctoral mentorship and indicate general needs for mentorship training. Similarly, a global construct assessment from postdoctoral trainees provides an alternative perspective on issues postdocs experience with their mentors. At the same time, individual items may be better used to assess more specific aspects of mentorship capabilities and needs, such as communication or professional development. The mentorship competency assessments for supervisors (MCA-ECS.S) and postdoctoral trainees (MCA-ECS.P) provide mentors and educational institutions opportunities to assess mentor competencies as self-evaluation and mentee-evaluation to understand better the education and training needs of postdoctoral mentors and trainees beyond the structure of graduate education systems.

This special session is based on empirical findings from an ongoing NSF CAREER Grant specializing in characterizing the mechanisms of attrition for engineering doctoral students. While many researchers have characterized “what” issues can cause students to be dissatisfied, fewer are working to understand how factors layer for individual students under individual conditions. Further, many faculty hold myths of graduate engineering attrition that are incomplete or do not cover both psychological and sociological perspectives. Therefore, this special session introduces graduate engineering attrition through the lenses of both theory and composite narratives constructed from the interviews we have conducted as part of this study. The special session is aimed at multiple stakeholders, and after the interactive session, participants will leave with access to resources and materials that will be useful to talk about leaving in productive and healthful ways than is typically common with the goal of reducing stigma and promoting the education for graduate students.

This research full paper addresses how different pathways into graduate school may correspond with students’ attributions of the factors that impact their attrition decisions. Studying attrition in graduate engineering programs is important to understand and benefit graduate engineering students in their programs. Pathway into to graduate study and its effect on themes in questioning departure from programs has not been widely studied. Returners, defined as students who had a gap of at least 6 months between undergraduate degree completion and graduate program entry, are often an understudied group in graduate engineering attrition. In this study, narrative analysis methods were employed to investigate potential differences between returners and direct-pathway students experiences as they questioned departure from their graduate engineering programs. In this work, we found that returners commonly face challenges in finding a support network and direct-pathway students commonly struggle with unclear goals in their studies. Providing returners with better support and helping direct-pathway students understand their goals can help students persist in their graduate engineering program.

This research paper serves as a benchmarking study to investigate the types and availability of resources available to postdoctoral scholars on university websites. Postdoctoral education in engineering and computer science disciplines is a forgotten stage of the academic pipeline, with very few scholars investigating the learning and development that occurs through the transient postdoctoral years. The few studies that have been done report postdocs feeling “forgotten” and on a “postdoctoral treadmill,” often without formal mentorship or guidance in developing the skills required to land academic careers. While most postdoctoral scholars do have supervisors to whom they report, most literature indicates that postdocs in engineering and computer science are still lacking mentorship in the peripheral skillsets essential for career success, and these effects are amplified for women and postdocs of color. Given a lack of interpersonal mentorship, it is plausible that postdocs turn to institutional resources for guidance and directions for professional development. To date, literature has not benchmarked the type or extent of resources available that are aimed at postdoctoral scholars. To this end, the purpose of this paper is to characterize university webpages using content analysis methods in order to understand the presence or absence of various types of support for postdocs at universities.

This research paper presents recent work exploring the power of natural language processing (NLP) methods applied to qualitative engineering education data. As NLP and other machine learning methods are developed for qualitative data, it is important to prioritize the role that theory plays in rigorous qualitative research, where the selection of a theoretical framework serves as the lens by which the research project is framed, results are analyzed, and findings are brought to light. Indeed, the view from a different theoretical lens can highlight novel or new findings. In this work, we seek to explore the viability of NLP methods for helping researchers select appropriate frameworks. In this work, we present our method to train a Python-based NLP algorithm to analyze an existing data set of interview data using one theoretical lens: Community of Practice theory, an oft-used theory in graduate education literature, which is the topic of the interview corpus to investigate. We present and test two methods for developing dictionaries by which to train the algorithm: An expert-curated dictionary and a machine-generated dictionary compiled by mining the theoretical framework sections of published literature employing Community of Practice theory. We apply these two dictionaries to analyze a corpus of 54 interview transcripts investigating graduate engineering attrition. The high dimensional data from NLP can be compared using Principal Component Analysis (PCA) visualization and pairwise distance plots to determine which method results in the most well-defined structure indicating agreement between the dictionary and the corpus of interview transcripts. In the discussion, we highlight opportunities for using these automated methods to help researchers with qualitative data analysis and warn against potential dangers and ethical ramifications for using machine learning and NLP for social science data. This work will have impact on the disciplinary communities working to embed computational language-based methods into engineering education research, and for the qualitative methods communities across social science and education disciplines.

With poor teaching quality being of concern in higher education in engineering, this Full Research Paper seeks to investigate an interview approach that aims to gain insight into an instructor's teaching methods and conceptions. This interview approach is called document elicitation, a method based on the interviewing approach of photo elicitation. Understanding an instructor's teaching conceptions and methods is useful in gaining insight into how and why people teach. Using document elicitation during an interview with two documents, 1) course syllabi, and 2) statements of teaching philosophy, this paper reports on the generative descriptions of teaching a syllabi and statements of teaching philosophy offer in a document elicitation setting. Semi-structured interviews were conducted with twelve assistant professors of engineering. The analysis focuses on the document elicitation portion of the interviews. Inductive-deductive thematic analysis was used to develop a codebook of teaching conceptions and methods. The paper reports on the teaching conceptions and methods that the participants describe, including active learning techniques, inclusive practices, and challenges. This paper may particularly interest people who conduct interviews for research and/or hiring purposes. Document elicitation as part of a hiring interview seems promising as a way to learn about a candidate's teaching conceptions and methods.

The purpose of this full research paper is to explore faculty perceptions on how departments and colleges can continue to support faculty through the associate professor level. Little research exists that discusses best practices or even possible formats for mentoring and faculty development programs at the associate professor level. This study begins to fill this gap for one institutional type, eliciting suggestions from faculty on desired characteristics and attributes of a mid-career (associate) faculty development program. To elicit feedback, five focus groups of between 5-9 tenured or tenure-track mechanical engineering faculty each were conducted to elicit information on what faculty at various levels knew about career development post tenure, and what their ideas of an “ideal” mid-career faculty development initiative would look like. Audio recordings and jottings were collected as data and analyzed through constant comparative methods and content analysis methods, using landscapes of practice theory to highlight areas where faculty noted the need for explicit professional development. Results indicate that perceptions on career advancement post-tenure differed between faculty at different levels, and indicate several potential structures, characteristics, and attributes that a successful potential midcareer faculty development model might have.

This research full paper explores interview data with N=36 engineering graduate students to understand the factors and characteristics of graduate socialization, with the effort of better preparing students to succeed in doctoral programs. This research is motivated by the alarming fact that nearly one-third of engineering doctoral students will not finish their PhD programs; however, little research has been conducted on the various factors that can lead to attrition or enhance persistence in graduate engineering programs. This paper presents the results from the interview phase of a larger study investigating doctoral engineering socialization, attrition, persistence, and career trajectories. The participants for this study come from large research-intensive universities across the United States, and were sampled for maximum variation in a number of different categories, including stage in their doctoral program, gender, and race. Upon collecting and analyzing interview data from our participants through constant comparative and content analysis methods, several themes arose including concerns for mental health in engineering graduate students and uncertainties with joining the culture of academia in their future careers. Further, although the participants for this study are currently graduate students who anticipate completing their PhDs, nearly half of the participants discussed strongly considered leaving at some point. This study adds to the body of literature surrounding engineering attrition and the underlying issues driving engineering PhDs away from academic engineering careers.

This research full paper proposes opportunities to expand qualitative and textual data analysis using natural language processing (NLP), and demonstrates opportunities to use NLP in engineering education work in our presentation of two NLP-based projects as examples of how NLP can be used. The discipline of engineering education frequently employs qualitative data analysis techniques, but fundamentally, the analysis of large corpora of textual documents is limited by researcher's time. In this paper, we present a brief review literature of how NLP has been used in other qualitative research fields. This portion of the paper is aimed to provide a clear description of NLP for those unfamiliar with machine learning and natural language processing methods. The second part of the paper will provide two brief examples of how NLP is being employed in our research group. Example 1 is a study of engineering résumés, with the intention of being able to calculate the “disciplinary discourse density” based off the engineering language presented in engineering résumés, a technique validated in prior qualitative studies by hand. Example 2 is a genre analysis of engineering literature reviews, seeking to understand the ways in which sentences linguistically build into arguments, such that the task of writing literature reviews might be demystified. This paper will have a methodological impact for researchers attempting to use NLP methods to analyze qualitative data, articulating the opportunities and barriers in using these methods for engineering education research.

At the graduate level, most milestones are based on the ability to write for an academic audience, whether that be for dissertation proposals, publications, or funding opportunities. Writing scholars often discuss the process by which graduate students learn to join their academic “discourse communities” through academic literacies theory. Graduate attrition researchers relate the feeling of belonging with persistence in doctoral programs; however, there has not to date been any research that directly studies engineering writing attitudes and perceptions with student career trajectories, persistence, or attrition. To meet this need, this paper presents research from a larger study analyzing graduate level engineering writing and attrition. The explicit objective of this paper is to present quantitative data relating current graduate engineering students' attitudes, processes, and concepts of academic writing with the certainty of their career trajectory. Five scales measuring aspects of writing were deployed to engineering programs at ten research intensive universities across the United States, with a final total of n=621 graduate student respondents that represent early-career, mid-career, and late-career stages of the graduate timeline. Results indicate that graduate student processes and conceptions of engineering writing correlate with the likelihood of pursuing careers in various engineering sectors after completing their graduate degree programs.

Though considerable attention and resources have been dedicated over a few decades to improve the representation of women in engineering fields, the issues of underrepresentation still exist, especially among the tenure track and research faculty. “Chilly” climate models of underrepresentation discuss these and other barriers to women's persistence, including implicit gender bias in the department. For women of color in faculty roles, these biases overlap to become serious barriers to persistence. Microaggressions in the classroom can lead to depreciation of contributions of women faculty, disregarding their accomplishments and limiting their effectiveness within social and educational contexts. One area that is underexplored are the microaggressions that occur intentionally or unintentionally in the engineering classroom when (young) women faculty teach upper-level or graduate courses in traditionally gendered subjects such as internal combustion engines, computer programming, or rocket propulsion. The purpose of this research is to qualitatively investigate experiences and strategies that junior women faculty in engineering disciplines use in the classroom to overcome student bias and microaggressions through content analysis of semi-structured interview data. This Work-in-Progress paper will present preliminary themes and strategies by which women faculty establish credibility with predominantly male students.

Engineering writing and communication is increasingly required of engineering undergraduate and graduate students; yet this call is rarely met through research-based methods. This research posits that deep investigation of the cognitive processes within engineering writing requires a novel method for data time-dependent analysis. However, no such method exists. This paper details our decision-making processes as we examine the available methods to analyze screen-capture video data of real time writing in an authentic engineering writing context. We detail the process of selecting the most appropriate approach and then provide a proof of concept for this method though analysis of screen capture video data of authentic engineering writing spanning several weeks. Complex issues in engineering education require creative interpretations of what can be used as “data,” and similarly, creative and rigorous solutions for analysis. The results of this paper directly affect researchers doing real-time analysis of video data in all respects, and will also help other engineering education researchers to analyze non-traditional data.

Undergraduate engineering students are often taught to create their engineering résumés early in their academic careers as part of first-year composition or technical writing classes. Often, these classes are not taught by engineering faculty, and few resources exist for engineering-specific résumé development. A corpus of 31 engineering résumés was collected, representing engineers across experience levels and engineering fields and analyzed through genre analysis methods in order to understand the linguistic mechanisms by which engineers present the merit of their work in a condensed résumé format in order to develop an intervention to help instructors of technical writing and first year engineering develop engineering résumés in a disciplinary context. The intervention facilitates student reflection on authentic engineering résumés leading them toward their own learning regarding effective presentation of engineering experiences on a résumé, as well as to encourage engineering students to plan effectively for the experiences that will help them achieve their desired careers.