PERC 2021 Abstract Detail Page
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| Abstract Title: | Predictors of faculty sentiment on their transition to online teaching |
|---|---|
| Abstract: | In the spring and summer of 2020, we collected data on faculty experiences during their transition to onlineteaching as a result of the COVID-19 pandemic. Data on the participants' institutions, job security and position,and preparation time were collected, as well as a free text response to add anything the participants felt wasrelevant. In total 364 text responses were collected. Using natural language processing tools, sentiment scoreswere calculated for each response. The sentiment was found to be overall positive. Then, a machine learningmodel was created using Keras, which was trained on various data for 75% of the responses. The remaining25% were used for predicting sentiment scores, to identify which data from the survey, if any, were potentialpredictors of participant sentiment. The score predictions were used to determine if any data on participantsâinstitutions, positions, or transition time were correlated with positive or negative experiences. |
| Abstract Type: | Contributed Poster Presentation |
| Session Time: | Poster Session 1 Room D |
| Poster Number: | 1D-12 |
Author/Organizer Information | |
| Primary Contact: |
Jillian Mellen Drexel University PA |
| Co-Author(s) and Co-Presenter(s) |
Eric Brewe Adrienne Traxler Colin Green Sarah Scanlin |
