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Exploring GTA Skills and Responsibilities to Inform a GTA Professional Development Program in Computer Science

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Faculty Development Division Poster Session

Tagged Division

Faculty Development Division

Page Count

5

Permanent URL

https://peer.asee.org/37142

Download Count

10

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Paper Authors

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Jill K. Nelson George Mason University

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Jill Nelson is an associate professor in the Department of Electrical and Computer Engineering at George Mason University. She earned a BS in Electrical Engineering and a BA in Economics from Rice University in 1998. She attended the University of Illinois at Urbana-Champaign for graduate study, earning an MS and PhD in Electrical Engineering in 2001 and 2005, respectively. Dr. Nelson's research focus is in statistical signal processing, specifically detection and estimation for applications in target tracking and physical layer communications. Her work on target detection and tracking is funded by the Office of Naval Research. Dr. Nelson is a 2010 recipient of the NSF CAREER Award. She is a member of Phi Beta Kappa, Tau Beta Pi, Eta Kappa Nu, and the IEEE Signal Processing, Communications, and Education Societies.

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Yutao Zhong George Mason University

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Mark Huntington Snyder George Mason University

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Elizabeth L. White George Mason University

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Dr. Elizabeth White is an associate chair and associate professor of Computer Science and a member of the C4I center at George Mason University in Fairfax, VA. She has a Ph.D. in Computer Science from the University of Maryland, College Park and a B.S. in Computer Science from the College of William and Mary in Virginia. Her research and teaching interests include compilers, embedded systems, software architecture, middleware and programming languages. Dr. White serves as director of the BS Computer Science and BS Applied Computer Science programs at George Mason University.

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Abstract

This work-in-progress paper describes the process and initial outcomes of an effort to identify and prioritize content for a newly established GTA training program in a computer science department. As part of an NSF-funded project that aims to transform teaching practices in highly enrolled gateway STEM courses, the computer science (CS) department at a research-focused state institution is working to integrate active learning practices in its CS 1 (freshman level) and CS 2 (sophomore level) courses. The combined courses have enrollments of nearly 1,000 students each semester, with lecture sections of 100-200 students and software lab sections of 25-30 students. Lab sections are led by GTAs, and hence GTA professional development plays a large role in transforming the teaching and learning approaches in these courses.

The CS department at the center of this study is growing rapidly, as the university in which it is housed is devoting significant resources to growing computing programs and emphasizing the importance of computing competencies across majors. As such, the number of GTAs needed to support courses in the CS department is also rapidly increasing, and finding students to fill GTA roles is sometimes difficult. New GTAs are often new graduate students, many of whom are enrolled at a US institution for the first time. Recognizing that nearly all of the CS GTAs (over 75 in total) face similar challenges related to a lack of training and/or experience in college teaching, the department aims to create a department-wide GTA training program.

To understand the main challenges faced by CS GTAs and to inform the development of a GTA training program that makes the most effective use of limited resources (specifically funding, GTA time, and instructor time), the CS department is surveying GTAs, as well as instructors whose courses are supported by GTAs. GTAs are asked what skills they view as most important to their success in fulfilling their GTA responsibilities and their perceived level of preparation for those responsibilities. GTAs’ perceived level of preparation provides a window into their teaching self-efficacy, which can be measured over time to track teaching development [1]. GTAs are also asked to describe what resources (including human) they access in an effort to prepare for their teaching responsibilities. Instructors whose courses are supported by GTAs are asked what they view as primary GTA responsibilities, the skills required to succeed in those responsibilities, and in what areas additional GTA preparation would be most valuable.

This paper will present the findings from analysis of the data collected from surveying CS GTAs and instructors. Analysis will identify common themes across GTA and faculty responses, as well as any relationships between GTA responses and factors such as GTA role (lab, recitation, course level, etc.), demographic information, and level of teaching experience. In addition, the paper will describe initial plans for the structure and content of department-wide GTA professional development as they emerge from analysis of survey results. We suggest that this work-in-progress paper be presented as a poster in order to support in-depth discussion with other participants who are also developing and/or studying GTA professional development programs.

[1] S.E. DeChenne, L.G. Enochs, and M. Needham, “Science, Technology, Engineering, and Mathematics Graduate Teaching Assistants Teaching Self-Efficacy,” Journal of the Scholarship of Teaching and Learning, Vol. 12, No. 4, December 2012.

Nelson, J. K., & Zhong, Y., & Snyder, M. H., & White, E. L. (2021, July), Exploring GTA Skills and Responsibilities to Inform a GTA Professional Development Program in Computer Science Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37142

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