A Text Analytical Study of STEM Inquiries in Grad Slam Competition
DOI:
https://doi.org/10.51355/j-stem.2025.185Keywords:
Grad Slam, Three Minute Thesis, Text Analytics, Communication SkillsAbstract
Grad Slam competitions offer a unique opportunity for students to showcase their STEM research. As a popular platform to promote STEM education in the United States, the competition limits presentations to three minutes, demanding rigorous training to improve graduate students' presentation skills. This research incorporates text analytics to extract the substance of Grad Slam projects across 2019-2022 and assess the effectiveness of Grad Slam training. Videos of Grad Slam presentations are transcribed to enable the use of Natural Language Processing to transform the unstructured text into normalized data suitable for processing by machine learning algorithms. R scripts are developed to disentangle the overall features of the Grad Slam outcomes. Survey data are analyzed to report student feedback about workshop preparation for project presentation. The text mining not only shows strong connections between Grad Slam presentation contents and STEM subject inquiries but also reflects a trend of strengthening research culture at a Hispanic Serving Institution in the United States. The survey feedback reconfirms the benefit of workshop training. The Grad Slam competition and its related workshops emerge as a unique platform, aside from STEM coursework, to help graduate students excel in their specialty fields, as well as establish confidence in communicating their research to a general audience.
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