An Investigation Into Secondary School Students' Debugging Behaviour in Python

2025-08-20

AI Summary

This study investigates cognitive difficulties and behavioral patterns in middle school students’ Python debugging to inform evidence-based debugging instruction. Analyzing fine-grained interaction logs from 73 students using an online code editor, we employed qualitative content analysis and inductive coding to identify prevalent inefficient strategies—such as random code modification and error-introducing edits—and their underlying causes. Introducing the novel theoretical lens of “fragile knowledge,” we reveal a critical cognitive gap: students often possess adequate error-localization ability but lack systematic error-remediation competence. Results indicate that debugging failure stems not from syntactic misconceptions, but from deficits in debugging metacognition and strategic repertoire. The findings advocate for integrating structured debugging pedagogy into compulsory computing education and provide empirical grounding and actionable pathways for designing debugging instruction aligned with computational thinking development. (149 words)

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Background and context: Debugging is a common and often frustrating challenge for beginner programmers. Understanding students' debugging processes can help us identify the difficulties and misunderstandings they possess. However, we currently have limited knowledge of how secondary students debug in a text-based language, a medium through which millions of students will learn to program in the future. Objectives: In this paper, we investigate the debugging behaviour of K-12 students learning a text-based programming language, as part of an effort to shape how to effectively teach debugging to these students. Method: We collected log data from 73 students attempting a set of debugging exercises using an online code editor. We inductively analysed these logs using qualitative content analysis, generating a categorisation of the debugging behaviours observed. Findings: A range of behaviours were exhibited by students, skewed towards being ineffective. Most students were able to partially locate errors but often struggled to resolve them, sometimes introducing additional errors in the process. We argue that students struggling to debug possess fragile knowledge, a lens through which we view the results. Implications: This paper highlights some of the difficulties K-12 learners have when debugging in a text-based programming language. We argue, like much related work, that effective debugging strategies should be explicitly taught, while ineffective strategies should be discouraged.

Problem

• Investigating secondary students' debugging behavior in Python
• Identifying difficulties and misunderstandings in debugging processes
• Shaping effective teaching methods for debugging strategies

Innovation

• Online code editor log analysis
• Inductive qualitative content analysis
• Debugging behavior categorization framework