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Abstract BackgroundEngineering curricula are built around faculty and accreditors' perceptions of what knowledge, skills, and abilities graduates will need in engineering careers. However, the people making these decisions may not be fully aware of what industry employers require for engineering graduates. Purpose/HypothesisThe purpose of this study is to determine how industry employer‐sought professional and technical skills vary among engineering disciplines and levels of education. Design/MethodUsing a large sample (n = 26,103) of mined job advertisements, we use the O*NET skills database to determine the frequencies of different professional and technical skills for biomedical, civil, chemical, electrical, environmental, and mechanical engineers with bachelor's, master's, and PhD degrees. ResultsThe most frequently sought professional skill is problem‐solving; the most frequently sought technical skills across disciplines are Microsoft Office software and computer‐aided design software. Although not the most frequently requested skills, job advertisements including the Python and MATLAB programming languages paid significantly higher salaries than those without. ConclusionsThe findings of this study have important implications for engineering program leaders and curriculum designers choosing which skills to teach students so that they are best prepared to get and excel in engineering jobs. The results also show which skills students can prioritize investing their time in so that they receive the largest financial return on their investment. 

Feedback is a critical aspect of improvement. Unfortunately, when there is a lot of feedback from multiple sources, it can be difficult to distill the information into actionable insights. Consider student evaluations of teaching (SETs), which are important sources of feedback for educators. These evaluations can provide instructors with insights into what worked and did not during a semester. A collection of SETs can also be useful to administrators as signals for courses or entire programs. However, on a large scale as in high-enrollment courses or administrative records over several years, the number of SETs can render them difficult to analyze. In this paper, we discuss a novel method for analyzing SETs using natural language processing (NLP) and large language models (LLMs). We demonstrate the method by applying it to a corpus of 5000 SETs from a large public university. We show that the method can extract, embed, cluster, and summarize the SETs to identify the themes they contain. More generally, this work illustrates how to use NLP techniques and LLMs to generate a codebook for SETs. We conclude by discussing the implications of this method for analyzing SETs and other types of student writing in teaching and research settings.