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IRE STEM Scholars program contributes to the national need for well-educated STEM professionals by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at Minnesota State University, Mankato. Over its six year duration, this project will fund scholarships to 120 unique full-time students. The IRE STEM Scholars Program provides a financially sustainable pathway for students across the nation to graduate with a Bachelor of Science degree in engineering and up to two years of industry experience. Students typically complete their first two years of engineering coursework at community colleges across the country. Students then join IRE and spend one transitional semester gaining training and experience to equip them with the technical, design, and professional skills needed to succeed in the engineering workforce; it is during this semester that students receive the S-STEM scholarship. During the last two years of their education, IRE students work in paid engineering co-ops, while being supported in their technical and professional development by professors, learning facilitators, and their own peers. The IRE STEM Scholars project financially supports low-income students during the transitional semester, which has two financial challenges: university tuition costs are higher than their previous community college costs, and the semester occurs before they are able to earn an engineering co-op income. In addition, the project provides personalized mentorship throughout students’ pathway to graduation, such as weekly conversations with a mentor. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates. As part of the scope of this project, a concurrent mixed-methods research study will be done on engineering students’ thriving, specifically their identity, belonging, motivation, and subjective wellbeing (or mental and physical health). This study will address the following research questions: How do undergraduate students’ engineering identity and belongingness develop over time in a co-op-based engineering program? How do undergraduate students’ motivation and identity connect to overall wellbeing in a co-op-based engineering program? Currently in its second year, the project has supported 20 students, including 6 students on co-op. These six students have been interviewed on their sense of belonging in engineering during their co-op experiences, and have provided multiple survey data points describing IRE students’ experiences in co-op and overall sense of belonging. These IRE STEM Scholars program participant-specific data along with survey data documenting the co-op experiences of all IRE students describe how co-op experiences can be used to provide a financially responsible pathway to an engineering degree for low-income, high achieving students. Future work will utilize these values to identify ways to better support the IRE STEM scholars’ identity development as they move into their first co-op experiences. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 

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. 

This Work-In-Progress research paper presents preliminary results and next steps of a study that aims to identify institutional data and resources that instructors find helpful in facilitating learning in large foundational engineering courses. The work is motivated by resource-driven compromises made in response to increasing engineering student populations. One such compromise is teaching some courses (usually foundational courses taken by students across multiple disciplines) in large sections, despite research suggesting that large class environments may correspond with unfavorable student learning experiences. Examples of courses often taught in large class environments are mathematics, physics, and mechanics. We are currently working with a cohort of instructors of foundational engineering courses as part of an NSF Institutional Transformation project. We have collected qualitative data through semi-structured interviews to explore the following research question: What data and/or resources do STEM faculty teaching large foundational classes for undergraduate engineering identify as being useful to enhance students' experiences and outcomes a) within the classes that they teach, and b) across the multiple large foundational engineering classes taken by students? Our inquiry and analysis are guided by Lattuca and Stark's Academic Plan Model. Preliminary analysis indicated that instructors would like more opportunities to interact and collaborate with instructors from other departments. These results will inform activities for our Large Foundational Courses Summit scheduled for Summer 2018 as part of the project.