Date
Publisher
arXiv
Pair programming is a widely used collaborative learning practice in computer science education yet its effectiveness varies substantially due to breakdowns in coordination attention and cognitive regulation between partners. This paper investigates whether AI supported feedback grounded in joint visual attention and joint mental effort can improve collaborative programming performance and how feedback timing shapes learner AI interaction. Two experimental studies using dual eye tracking capture real time indicators of collaborative regulation during debugging tasks. Study 1 examines reactive feedback that intervenes when observed joint visual attention or joint mental effort deviates beyond predefined thresholds while Study 2 evaluates proactive feedback that forecasts future regulatory breakdowns using machine learning models and intervenes pre emptively. Across both studies feedback effectiveness is assessed through debugging success time on task and feedback uptake reflected in code changes. Multimodal feedback significantly improves collaborative performance compared to no feedback conditions. Reactive feedback yields strong gains in debugging success and efficiency particularly when joint visual attention and joint mental effort based feedback are combined. Proactive forecast based feedback further enhances performance reduces time on task and increases constructive feedback uptake while relying less on intrusive interventions. Proactive feedback better preserves learner agency by maintaining optimal collaboration states, particularly for high-performing pairs. These findings demonstrate that gaze and mental effort synchrony can serve as reliable actionable triggers for AI supported collaborative learning highlighting the importance of feedback timing transparency and anticipatory regulation in supporting effective pair programming.
Study at a glance
- Question: Can AI feedback on shared gaze and mental effort improve pair programming debugging performance?
- Setting and data: Two experiments with pairs completing debugging tasks using dual eye-tracking; sample size, location, and year not stated in the abstract.
- Method: Two experiments comparing reactive and machine-learning-based proactive feedback to no feedback conditions (Lab / online experiment)
Why it matters
Pair programming effectiveness varies substantially because partners often experience breakdowns in coordination, attention, and cognitive regulation.
Key findings
- Multimodal feedback significantly improved collaborative programming performance compared to conditions without feedback.
- Reactive feedback produced strong gains in debugging success and efficiency, especially when combining visual attention and mental effort indicators.
- Proactive feedback forecasting regulatory breakdowns further boosted performance, reduced task time, increased constructive feedback uptake, and preserved learner agency, particularly for high-performing pairs.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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