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This paper details a poster presented in the National Science Foundation (NSF) Grantees Poster Session for the 2022 ASEE Annual Conference. The study, aptly titled, aims to examine the ‘Long-Term Effect of Involvement in Humanitarian Engineering Projects on Student Professional Formation and Views of Diversity, Equity, and Inclusion (DEI).’ As part of the larger study, this poster details the results from alumni (n=19) of the Lipscomb University engineering program collected through an open-ended questionnaire. The research team performed an inductive coding analysis of the qualitative data to understand the connections between humanitarian engineering projects, professional formation, and views of DEI. Quantitative results as well as data from other participant groups, including current students and non-alumni engineering professionals, will be presented elsewhere. Emergent codes showed that participants found both outward and inward value in serving others. Outward value reflected a better quality of life for the person benefiting from service while inward value provided personal satisfaction, learning, or growth for the participant. This inward value was also evident with respect to views of DEI where participants mentioned learning or growing from past events. Two participants directly mentioned a connection between their experiences with humanitarian engineering projects and their views of DEI. Additionally, the codes connected to existing literature in engineering education as well as theories like empathy, identity development, and emotional intelligence. These results are promising for this study and will be expanded upon through interviews where these connections will be examined at a deeper level. 

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.