AI & ML
Context-Specific Instruction: A Longitudinal Study on Debugging Skill Acquisition and Retention for Novice Programmers
arXiv cs.SE0 views
arXiv:2509.22420v2 Announce Type: replace Abstract: Bug localization is a critical skill, yet novices often lack systematic approaches. Prior work tested abstract guidelines and general concrete steps; the impact of context-specific instruction is unclear. We ran an eight-week longitudinal study with four conditions: no instruction (G1), abstract guidelines (G2), concrete steps (G3), and our context-specific instruction that pairs concrete bug-localization steps with problem-specific details (G4). Forty-four undergraduates participated; 41 completed all five sessions (S1-S5). Each session included 2-3 debugging tasks to identify the minimal code element containing a seeded logical fault. We measured correctness (binary), time to completion, self-perceived scores (stress, difficulty, satisfaction, and strategy adherence). G4 achieved higher correctness and shorter time to completion: it reached 80% correctness after one session (vs. 20-44% for other groups) and maintained 80% after three weeks, outperforming all groups (p < 0.05); its time to completion stabilized at 13-15 minutes in S1, whereas other groups took 2-3 sessions to stabilize at 22-27 minutes. Qualitative responses showed lower stress and higher satisfaction in G4, with participants internalizing strategies via contextual examples. We conclude that context-specific instruction yields faster skill acquisition and stronger retention than abstract guidelines or context-agnostic steps. Even 1-2 sessions produced significant gains, while extended practice optimized and stabilized performance. Integrating contextual examples with abstract principles may bridge theory-practice gaps in bug-localization education and provide a more equitable path for novices.
Source:https://arxiv.org/abs/2509.22420PreviousRiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM-Generated CodeNextInstruction-Tuning Open-Weight Language Models for BPMN Model Generation
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