Designing TA Training for Computer Science Graduate Students: Remote and Self-paced Options for A Supported Introduction to Reflective Teaching

Abstract

Teaching Computer Science in public, research-focussed institutions is often a team effort, with graduate students serving key roles as teaching assistants. These graduate students come from diverse backgrounds and educational experiences. Many of them have not taken the course they are TAing, or took it at their undergraduate institutions under different norms and practices. We developed a course for first-time Computer Science TAs to help them navigate the mechanics of their role while learning evidence-based teaching and reflective practices. After the recent pandemic-induced emergency shift to remote learning, we introduced and evaluated three new modalities of delivering this course: synchronous, fully self-paced, and hybrid pacing. We find that student satisfaction with all the alternate versions of the course was generally positive and is not correlated with modality. We conclude that Computer Science departments have significant flexibility in developing formats for delivering TA training courses, and that these courses are valued by first-time Computer Science graduate student TAs.