A Socio-Technical Approach to Feedback and Instructional Development for Teaching Assistants

Teaching assistants (TAs) in the United States play a prominent role in educating undergraduates. Their influence can make the difference between students continuing in their majors or leaving them. However, most TAs use teacher-centered, transmission models of teaching, i.e., lecturing to disengaged students. Part of the reason for this is that most TAs receive little training on how to teach,<br>and almost no grounded feedback about their teaching behaviors. In this thesis I describe my work investigating the use of technology to increase feedback and training<br>for TAs. My focus is understanding how their knowledge, skills, beliefs, and attitudes should drive the<br>design of algorithms for gathering classroom behavioral data and delivering computer-mediated feedback and consultation. My work evaluates a novel framework for investigating how TAs interact with their data, reflect on what it means, and decide what (if anything) to change in their teaching. I examine how initial beliefs can impact their system interactions, how those beliefs change over time,<br>and the resulting implications for designing data-driven training artifacts.