Title:Assessing the Impact of Disorganized Background Noise on Timed Stress Task Performance Through Attention Using Machine-Learning Based Eye-Tracking Techniques

Abstract:Noise pollution has been rising alongside urbanization. Literature shows that disorganized background noise decreases attention. Timed testing, an attention-demanding stress task, has become increasingly important in assessing students' academic performance. However, there is insufficient research on how background noise affects performance in timed stress tasks by impacting attention, which this study aims to address. The paper-based SAT math test under increased time pressure was administered twice: once in silence and once with conversational and traffic background noise. Attention is negatively attributed to increasing blink rate, measured using eye landmarks from dLib's machine-learning facial-detection model. First, the study affirms that background noise detriments attention and performance. Attention, through blink rate, is established as an indicator of stress task performance. Second, the study finds that participants whose blink rates increased due to background noise differed in performance compared to those whose blink rates decreased, possibly correlating with their self-perception of noise's impact on attention. Third, using a case study, the study finds that a student with ADHD had enhanced performance and attention from background noise. Fourth, the study finds that although both groups began with similar blink rates, the group exposed to noise had significantly increased blink rate near the end, indicating that noise reduces attention over time. While schools can generally provide quiet settings for timed stress tasks, the study recommends personalized treatments for students based on how noise affects them. Future research can use different attention indices to consolidate this study's findings or conduct this study with different background noises.