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Investing in the Future: A Financial Comparative Study of HSIs, HBCUs, and Non-Minority Serving Liberal Arts Colleges
Get PDFThis research presents a preliminary expedition into identifying the relationship between Net Present Value (NPV) and Return on Investment (ROI) of general liberal arts institutions, Hispanic Serving Institutions (HSIs), and Historically Black Colleges and Universities (HBCUs). HSIs and HBCUs have a long tradition of serving specific underrepresented communities of color, fostering success through culturally appropriate learning environments. This research utilizes a unique business lens to discuss the value of a service-based institution, using tools such as value proposition and consumer fit, and then, it compares each set of institutions using ROI and NPV. The data, sourced from College Scorecard, Georgetown University, and Data USA, is consolidated into a unique dataset composed of four-year universities that predominantly grant bachelor’s degrees in non-rural Texas, with a small (642) to small-medium (4,547) student body, and coincidentally, religiously affiliated. The outcomes of this research reveal concerning trends. While HSIs and liberal arts institutions exhibit similar NPVs over a 40-year period, HBCUs start from a lower NPV and experience slower growth. This disparity suggests a potential financial disadvantage for HBCU graduates. Similarly, HBCUs have lower ROIs, even when compared by majors to the other institutions. This research demonstrates a disparity in post-graduation achievement and in metrics of NPV and ROI, but further research is necessary to determine where the gap originates
Leaf it to the AI: visualizing foliage with machine learning
Get PDFThis project sets out to create a tool to help aid in the research and monitoring of the threatened prickly cacti [Fig. 1]. due to an invasive species of insect growing to be a threat. This website displays current and predictive locations of foliage within a region.
To get the data of the predictive location, an additional program was developed that uses a machine learning model to provide coordinates of potential areas where more plants may be. The computer program provides a visualization of potential patterns for the foliage in the database. The machine learning model combs through the data and provides patterns for researchers. The website provides visualizations by comparing the output of similar locations.https://commons.stmarytx.edu/rscpos25/1021/thumbnail.jp
Lawtina Network Summit, St. Mary\u27s University School of Law, 2024
No full texthttps://commons.stmarytx.edu/lawtina2024/1013/thumbnail.jp