Assessment of indoor ambient noise level in school classrooms
Invited paper
Politecnico di Torino
Tuesday 2 june, 2015, 10:40 - 11:00
0.9 Athens (118)
Abstract:
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 teacher’s 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.