| CONSTRUCTION MANAGEMENT |
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Impact of construction industrial noise on workers' learning efficiency: A study based on electroencephalogram analysis |
| CAO Xinying1, ZHENG Decheng2, QIN Peicheng1, LI Xiaodong3 |
1. School of Civil and Architectual Engineering, Hainan University, Haikou 570228, China;
2. International Business School, Hainan University, Haikou 570228, China;
3. Department of Construction Management, Tsinghua University, Beijing 100084, China |
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Abstract [Objective] In this study, the impact of industrial noise on the learning efficiency of construction industry workers was investigated. Twenty-nine workers from a prefabricated component factory were recruited as study participants. [Methods] Industrial noise is generally classified as steady noise and complex noise. Each group of participants was randomly assigned to four different noise interference conditions of one of the two noise types, which were as follows:control group (daily white noise), low noise group (60 dB(A)), medium noise group (70 dB(A)), and high noise group (80 dB(A)). Each experimental session included a 5-minute instructional video and 20 multiple-choice questions related to the video content. The instructional video and test questions were sourced from the National Prefabricated Construction Vocational Skills Competition Exam Question Bank. Furthermore, the content of the instructional videos was designed by training instructors at the component factory to address the practical knowledge needs of the workers, and this content was synthesized into instructional videos. The test questions included knowledge points extracted from the instructional videos. Each instructional video and its corresponding test questions had similar levels of difficulty, with text quantity differences controlled within 10%. During the experiment, electroencephalogram (EEG) data were collected using Emotive-EPOC X EEG equipment. After preprocessing, including filtering, bad segment removal, and independent component analysis, the power spectral density (PSD) values for various frequency bands were extracted from the EEG data. Subsequently, these PSD values were utilized to evaluate the attention and cognitive load levels of the participants during the experiment. Behavioral data (accuracy and reaction time) were collected with psychological software and compared with the cognitive state data. Kruskal-Wallis variance analysis and Mann-Whitney U tests were employed to compare the differences in behavioral data and cognitive state data among the different groups. Pearson's correlation coefficient was utilized to evaluate the relationships between various data sets. [Results] The results of the comparative analysis reveal that, compared to a steady noise environment, a complex noise environment results in lower accuracy and longer reaction times, indicating that complex noise environments are more likely to reduce the learning efficiency of workers. However, within the same noise type, the noise level does not substantially affect the learning efficiency of workers. Moreover, different noise types have no remarkable effect on the cognitive state of workers, and different noise levels in a complex noise environment have no considerable differences in their impact on the worker's cognitive state. In a steady noise environment, different noise levels do not significantly affect cognitive load; however, they do considerably impact attention which significantly decreases when the noise reaches 80 dB(A). Correlation analysis reveals that attention is negatively correlated with cognitive load, and there is no significant correlation between reaction time and accuracy. Attention and cognitive load are not correlated with accuracy. Attention is negatively correlated with reaction time, while cognitive load is positively correlated. This implies that engaging in cognitive tasks increases the workers' cognitive load, leading to decreased attention levels and longer reaction times. Therefore, this study confirms the mediating effect of cognitive states on the relationship between noise and workers' learning efficiency. [Conclusions] This work offers a scientific basis for developing targeted measures to reduce the impact of noise on the cognitive states of construction workers and to enhance their learning efficiency.
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| Keywords
construction industrial noise
construction industry workers
learning efficiency
electroencephalogram (EEG)
cognitive state
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| About author: 曹新颖(1986-),女,副教授。
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Issue Date: 28 December 2023
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2024,
Vol. 64
