Ball, Michael and DeOrio, Andrew and Hsia, Justin and Blank, Adam (2021) Teaching TAs to Teach: Strategies for TA Training. In: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. Association for Computing Machinery , New York, NY, pp. 461-462. ISBN 9781450380621. https://resolver.caltech.edu/CaltechAUTHORS:20210310-103221223
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Abstract
"The only thing that scales with undergrads is undergrads". As Computer Science course enrollments have grown, there has been a necessary increase in the number of undergraduate and graduate teaching assistants (TAs, and UTAs). TA duties often extend far beyond grading, including designing and leading lab or recitation sections, holding office hours and creating assignments. Though advanced students, TAs need proper pedagogical training to be the most effective in their roles. Training strategies have widely varied from no training at all, to semester-long prep courses. We will explore the challenges of TA training across both large and small departments. While much of the effort has focused on teams of undergraduates, most presenters have used the same tools and strategies with their graduate students. Training for TAs should not just include the mechanics of managing a classroom, but culturally relevant pedagogy. The panel will focus on the challenges of providing "just in time", and how we manage both intra-course training and department or campus led courses.
Item Type: | Book Section | |||||||||
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Additional Information: | © 2021 Copyright is held by the owner/author(s). | |||||||||
Subject Keywords: | Teaching assistants; graduate student instructors; TA training; pedagogy; undergraduate student instructors | |||||||||
DOI: | 10.1145/3408877.3432579 | |||||||||
Record Number: | CaltechAUTHORS:20210310-103221223 | |||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20210310-103221223 | |||||||||
Official Citation: | Michael Ball, Andrew DeOrio, Justin Hsia, and Adam Blank. 2021. Teaching TAs to Teach: Strategies for TA Training. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE '21). Association for Computing Machinery, New York, NY, USA, 461–462. DOI:https://doi.org/10.1145/3408877.3432579 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 108383 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | Tony Diaz | |||||||||
Deposited On: | 10 Mar 2021 18:43 | |||||||||
Last Modified: | 16 Nov 2021 19:11 |
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