Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs’ ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.

Undergraduate Teaching Assistants (TAs) are an extremely important part of many Computer Science (CS) courses. They can provide personalized one-on-one or small-group guidance to students when it is not feasible for the professor to do so due to the scale of the class.

Teaching assistants (TAs) play a crucial role in Computer Science courses. When a student is stuck or confused, they often rely on a TA to help them understand a concept or debug their program. At the same time, TAs in Computer Science courses are often very new at teaching, and somewhat new at programming. They may lack the knowledge and resources necessary to help students learn effectively. This work seeks to better understand the nature of TA-student interactions and identify potential opportunities for improvement. We conducted an observational study of one-on-one TA-Student interactions during office hours of a Computer Science course, and analyzed these interactions through the lens of known practices of effective one-on-one tutors. We found that TA-Student interactions focus on code over concepts, and this focus may be detrimental to TAs’ use of good tutoring practices.

Code Puzzles can be an engaging way to learn programming concepts, but getting stuck in a puzzle can be discouraging when no help or feedback is available. Teachers and facilitators can alleviate this problem in a classroom setting, but it can be hard for teachers to keep track of who needs help and who is likely to resolve their problem on their own, especially in a large classroom. This work is a step toward helping teachers optimize their time by automatically gauging which students may benefit from an intervention at any given time. We use information about the bugs present in student code to predict which students are more likely to abandon the puzzle or take too long in solving it. Ultimately, we envision that teachers could use these predictions to make decisions about whom they should help next, and how.

We analyze existing data of students completing coding puzzles through the lens of a debugging process, in order to study the impact of different types of errors that students make as they solve the puzzle. We develop a scheme for categorizing the errors present in the student code at any given time, and use it to create a taxonomy of the trajectories that students take to arrive at the correct solution. We find that these metrics are expressive enough to capture important distinguishing characteristics of students' puzzle-solving strategies.