Pyzzles: Towards the Design of a Zugzwang-Inspired Learning Tool for Novice Programmers and its Effect on Debugging Skills and Self-Perceived Debugging Confidence

Abstract

This paper presents the design and preliminary results of Pyzzles (Python Puzzles), a web-based application inspired by Zugzwang principles from chess to enhance novice programmers' debugging skills. In Zugzwang puzzles, a player must decide on the move that most benefits their position. Pyzzles replicates this concept by providing learners with code snippets containing debugging errors and defining the "best debugging move" as the minimal number of lines edited to correct the code. Partial points are awarded for suboptimal corrections. Pyzzles was tested on a small cohort of 16 participants from introductory programming classes over 14 days. While Pyzzles significantly increased users' self-perceived debugging confidence, there was no significant improvement in actual debugging skills, defined as the ability to identify erroneous lines of code. The study's small sample size limits the generalizability of these findings, and the paper concludes with suggestions for future research to better integrate Pyzzles into the broader literature on debugging tools and to explore enhancements that could support both self-perceived confidence and actual debugging proficiency.