Teaching Assistant (TA) training is often the first (and sometimes only) opportunity that future faculty have to develop pedagogical and professional skills in an academic setting. Furthermore, TAs often interact most directly with undergraduate students in many courses, particularly large enrollment introductory courses, making them an important intervention point for early career students. Though many successful models exist, they are very context-specific; generalized best practices in TA training are yet to be well defined in the literature. In this project, we continue the work from our previous ASEE presentation in which we compare TA training programs between institutions. Our initial observations of the College of Engineering at the University of Wisconsin- Madison and the Chemical Engineering Department at Imperial College London have found that there are some demonstrated best practices in training TAs, such as peer moderated presentation skills sessions, but there are also many components that must be individualized to institutions based on context, size, and other needs. To gain a more robust and transferable set of recommendations, we are now expanding this discussion to several more higher education institutions as well as other departments within our own colleges. To facilitate this, we have developed a question framework to collect data on how academics have built their TA training programs, how they incentivize participation, what content they include, and more. By doing so we can develop a model that describes how institution size, location, and cohort demographics influence TA training, as well as identify universal best practices in TA training. By building a network of support between TA trainers we can ultimately share strategies to overcome common challenges and assemble a set of materials, activities, and other resources to be used by trainers to improve existing programs or develop new ones. This paper is being submitted as an Evidence-Based Practice Paper and would be best disseminated with the ASEE audience as a talk rather than a poster.
The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025