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Abstract Title: Applying Natural Language Processing to COVID Transition Surveys
Abstract Type: Symposium Talk
Abstract: In addition to extracting themes and content, Natural Language Processing techniques can also be used to analyze sentiment within text. Using sentiment analysis and thematic analysis, our present project aims to understand physics faculty responses to transition to online teaching during the COVID-19 pandemic. We surveyed physics faculty following the Spring 2020 and Fall 2020 term, and used Latent Dirichlet Allocation to extract topics from the responses. This analysis revealed that while the mean change in sentiment was found to be approximately zero; there was a distinct shift in themes from Spring to Fall. The topics found in the initial survey largely revolved around technological and cheating difficulties experienced by the instructors. The topics were noticeably different in the follow up survey with showing themes related to reflection and successful and sustainable practices.
Session Time: Parallel Sessions Cluster III
Room: Vandenberg A
Parallel Session: Machine learning methods in PER: Intuition and methodological discussion

Author/Organizer Information

Primary Contact: Colin Green
Drexel University