Debugging is a demanding aspect of programming yet guidance on how to teach it effectively remains limited. Novices often struggle to recognize impasses regulate their problem solving and manage cognitive load and stress. This study investigates whether real time multimodal feedback triggered by indicators of cognitive load and physiological stress can improve debugging performance narrow expert novice gaps and reduce the influence of prior programming experience on success. We conducted a between subjects experiment with 120 undergraduate computer science students who debugged a medium sized Python program. Participants were assigned to one of four conditions no feedback cognitive load triggered feedback stress triggered feedback or combined trigger feedback. Eye tracking and heart rate variability data were used to detect moments of struggle and automatically deliver brief context sensitive hints. All three feedback conditions significantly improved debugging success and efficiency compared with the control group. Cognitive load triggered feedback produced stronger gains than stress triggered feedback and the combined trigger condition yielded the largest improvements. Programming expertise predicted performance only in the control condition and in all feedback conditions the novice expert gap was markedly reduced. Adaptive feedback that responds to learners cognitive and affective states can help manage debugging demands and reduce performance differences linked to prior experience highlighting opportunities for physiologically aware adaptive learning environments.

翻译：调试是编程中极具挑战性的环节，但关于如何有效教授调试的指导仍然有限。新手常难以识别瓶颈、调节问题解决过程，以及管理认知负荷与压力。本研究探究了由认知负荷和生理压力指标触发的实时多模式反馈是否能提升调试表现、缩小专家-新手差距，并降低先前编程经验对成功的影响。我们开展了一项组间实验，120名计算机科学本科生参与调试一个中等规模的Python程序。参与者被分配到四种条件之一：无反馈、认知负荷触发反馈、压力触发反馈或组合触发反馈。利用眼动追踪和心率变异性数据检测困难时刻，并自动提供简短的情境敏感提示。与对照组相比，所有三种反馈条件均显著提升了调试成功率和效率。认知负荷触发反馈的效果优于压力触发反馈，而组合触发条件带来的改进最大。在控制条件下，编程专长仅能预测表现；在所有反馈条件下，新手-专家差距显著缩小。对学习者认知和情感状态做出响应的自适应反馈有助于管理调试需求并减少与先前经验相关的表现差异，这凸显了生理感知自适应学习环境的潜力。