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Student during-learning data such as think-alouds or writing are often coded for use of strategies or moves, but less often for what knowledge the student is using. However, analyzing the content of such products could yield much valuable information. A promising technique for analyzing the content of student products is semantic network analysis, more widely used in political science, communication, information science, and some other social science disciplines. We reviewed the small literature on semantic network analysis (SemNA) of individuals with relevant outcomes to identify which network analysis metrics might be suitable. The Knowledge Integration (KI) framework from science education is discussed as focusing on amount and structure of student knowledge, and therefore especially relevant for testing with SemNA metrics. We then re-analyze three published think-aloud data sets from undergraduate students learning introductory biology with the metrics found in the literature review. Significant relations with posttest comprehension score are found for number of nodes and edges; degree and betweenness centrality; diameter, and mean distance. Inconsistent results possibly due to text-specific features were found for number of clusters, LCC, and density, and null results were found for PageRank centrality and centralization degree. Basic principles from the KI framework are supported—amount of information (nodes), connections (edges, average degree), key ideas (degree and betweenness centrality) and length of causal chains (mean distance and diameter) are related to posttest comprehension, but not density or LCC. Possible explanations for slight variations across data sets are discussed, and alternative theories and metrics are offered. 

Doctoral students experience high rates of mental health distress and dropout; however, the mental health and wellness of engineering doctoral students is understudied. Studies of student persistence, wellness, and success often aggregate fields together, such as by studying all engineering students. Thus, little work has considered the experiences of biomedical engineering (BME) doctoral students, despite differences between doctoral BME research, course content, and career expectations compared with other engineering disciplines. In this qualitative interview case study, we explore stressors present in the BME graduate experience that are unique from engineering students in other disciplines. Methods We analyzed a longitudinal interview study of doctoral engineering students across four timepoints within a single academic year, consisting of a subsample (n=6) of doctoral students in a BME discipline, among a larger sample of engineering doctoral students (N=55). BME students in the sample experienced some themes generated from a larger thematic analysis differently compared with other engineering disciplines. These differences are presented and discussed, grounded in a model of workplace stress. Results BME participants working in labs with biological samples expressed a lack of control over the timing and availability of materials for their research projects. BME participants also had more industry-focused career plans and described more commonly coming to BME graduate studies from other fields (e.g., another engineering major) and struggling with the scope and content of their introductory coursework. A common throughline for the stressors was the impact of the interdisciplinary nature of BME programs, to a greater extent compared with other engineering student experiences in our sample. Conclusions We motivate changes for researchers, instructors, and policymakers which specifically target BME students and emphasize the importance of considering studies at various unit levels (university department level vs college level vs full institution) when considering interventions targeting student stress and wellness. 

Recent international calls have been made to build capacity in engineering by increasing the number of scholars using research-based instructional practices in engineering classrooms. Training traditional engineering professors to conduct engineering education research (EER) supports this goal. Previous work suggests that engineering professors interested in perform­ing social sciences or educational research require structured support when making this transition. We interviewed 18 professors engaged with a grant opportunity in the United States that supports professors conducting EER for the first time through structured mentor­ship. Thematic analysis of interview data resulted in four findings describing common percep­tions and experiences of traditional engineering professors as they begin to conduct formalised EER: motivation to conduct EER, institutional support and barriers, growth in knowl­edge, and integrating with EER culture. Within these findings, barriers to entering EER were uncovered with implications for professors interested in EER, funding agencies, and prospec­tive mentors, resulting in suggestions for improving access to EER for professors developing as teaching scholars. 

Reports on results from the first year of the RFE project, in which PhD engineering students were interviewed about stressors.