Assessment of indoor ambient noise level in school classrooms
Politecnico di Torino
Tuesday 2 june, 2015, 10:40 - 11:00
0.9 Athens (118)
Previous studies have shown that poor acoustic conditions inside classrooms interfere with an optimal teaching-learning process. Since high background noise levels and long reverberation times cause higher vocal use among teachers and lower understanding among students, it is recommended to guarantee physical conditions inside the classrooms that ensure optimal quality for teaching and learning. To reach this goal, it is important to characterize and control two main factors, namely reverberation and noise. This work focuses on measurement and analysis procedures of background noise level during primary school classroom activities. The main objective of this investigation is to assess a method which allows to optimize the procedures (data acquisition and elaboration) related to background noise measurement in real environments. A cross-sectional study among 29 Italian female teachers for a total of 20 classrooms with measured acoustics characteristics within 3 schools was conducted. School buildings had different architectural features, so classroom acoustics changed significantly between different environments. Background noise was monitored for the entire duration of classroom activities by positioning a calibrated sound level meter near to the teacherís desk, at least one meter far from every surface. From the long-term measurements (LTM) which were analyzed, short-term measurements (STM) of specific academic activities were also extracted (lecture, group activities, shared lessons, etc.). LTM and STM were elaborated in terms of A-weighted equivalent sound pressure level (LA,eq) and of A-weighted statistical sound pressure level (LA,90 which corresponds to the level which is overtaken for the 90% of the measurement duration). After every monitored activity teachers filled in a questionnaire on work- related conditions. The correlation between self-reports of BNL perception, LTM and STM was determined by the Kappa coefficient and receiver operating characteristic curves. Multivariate logistic regression analysis was used to determine associations between self-reports of BNL, LTM and STM.
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