Graduate teaching assistants (GTAs) in science, technology, engineering, and mathematics (STEM) often teach introductory lecture and laboratory sections in large, research intensive institutions. They are frequently left insufficiently supported inconsistent levels of professional development. Previous work using the TeachLivE mixed-reality teaching simulator showed that when GTAs rehearse teaching skills, they are able to transfer the practices to their own classes and continuously implement the new skills. International teaching assistants (ITAs), however, continued to report feeling under supported even with the simulator training. We then decided to interview ITAs about the skills that would be most impactful for them to practice, like asking and answering student questions and handling linguistic differences between themselves and their students. In this dissertation, I interview several ITAs to determine what teaching skills would be the most impactful for them to practice and then create mixed-reality simulator modules based on their requests. During individual interviews and use of newly created modules, a focus on anxiety emerged when ITAs reported anxiety when teaching. I adopted Anxiety-Uncertainty Management theory (AUM) to address anxiety related to communicating with students, and surveyed 191 GTAs. Statistically significant correlations between anxiety, uncertainty, and willingness to try various teaching techniques found in active learning were found. Using those correlations, I developed a five-week virtual community for teaching assistants who identify as having high teaching anxiety. During the community, we learned that discussing social and emotional needs allows for a more holistic approach to professional development. This work addresses needs of graduate students that are currently unsupported using traditional pedagogical trainings.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2755 |
| Date | 15 August 2023 |
| Creators | Sona, AJ |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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