Anti-patterns in Students' Conditional Statements

TL;DR

This study analyzes the prevalence of anti-patterns in beginner Python students' code, revealing common issues in conditional statements and providing insights for educational improvements.

Contribution

It offers a detailed analysis of anti-patterns in student code and suggests targeted educational strategies to improve coding practices.

Findings

High occurrence of 'if/else return bool' and 'confusing else' anti-patterns

Nearly 60% of anti-patterns are from two specific types

Anti-patterns are consistent across various exercises

Abstract

Producing high-quality code is essential as it makes a codebase more maintainable, reducing the cost and effort associated with a project. However, students learning to program are often given short, automatically graded programming tasks that they do not need to alter or maintain in the future. This can lead to poor-quality code that, although it may pass the test cases associated with the problem, contains anti-patterns - commonly occurring but ineffective or counterproductive programming patterns. This study investigates anti-patterns relating to conditional statements in code submissions made by students in an introductory Python course. Our primary motivation is to understand the prevalence and types of anti-patterns that occur in novice code. We analyzed 41,032 Python code submissions from 398 first-year students, using the open-source "qChecker" tool to identify 15 specific anti-patterns related to conditional statements. Our findings reveal that the most common anti-patterns are "if/else return bool", "confusing else", and "nested if", with "if/else return bool" and "confusing else" alone constituting nearly 60% of the total anti-patterns observed. These anti-patterns were prevalent across various lab exercises, suggesting a need for targeted educational interventions. Our main contribution includes a detailed analysis of anti-patterns in student code, and recommendations for improving coding practices in computing education contexts. The submissions we analyse were also collected prior to the emergence of generative AI tools, providing a snapshot of the issues present in student code before the availability of AI tool support.