Learning to program is challenging, because it involves novel skills. In contrast to the majority of work focusing on code generation, we target the skill of code tracing. Code tracing involves simulating the high-level actions a computer takes when it executes the program, including the flow of execution through it and how variable values change as a result. Code tracing supports program comprehension and generation. However, many students do not code trace effectively. Our project investigates the utility of peer tutoring for the learning of code tracing. While this approach remains untested in this context, it has successfully been used in other domains to improve student learning. We will present qualitative data from a case study involving students tutoring each other to code trace. This work is the first step in the context of a broader project focused on identifying ways to help students learn to code.

A fundamental skill needed to learn to program is code tracing, which involves simulating the steps a computer takes when executing a program to predict how it will behave and what output it. While code tracing helps students understand and write programs, students find this activity challenging. However, to date, little work exists on how to best support novice programmers in this activity. To address this gap, the high-level goal of our research is to investigates the utility of peer-tutoring for learning to trace programs. In our prior work, we conducted a preliminary case study exploring qualitative features of peer tutoring interactions. As the next step, we conducted an experimental study with two conditions: code tracing in a peer tutoring context vs. code-tracing alone. We present results from this study, focusing on quantitative results related to learning outcomes in each condition.

Code tracing involves simulating at a high level the steps a computer takes when it executes a computer program. This is a fundamental skill needed for programming activities, but one that novices find challenging. Thus, work is needed on how to support novice programmers in this activity. We conducted an experimental study with university students (N = 56) learning to code trace in two conditions, namely peer tutoring and solo code tracing. Our primary outcome variable was learning. However, since how students feel is also an important factor in educational settings, we also measured student emotion. Contrary to prior work in other domains, there was no significant benefit of peer tutoring and self-reported levels of emotion were similar in the two conditions; Bayesian statistics provided evidence for the null model in the majority of cases.