Abstract

Educators have developed innovative teaching strategies in order to maximize learning outcomes in classrooms. Active learning classrooms are new learning spaces that facilitate the teaching strategies with enhanced students’ engagement and collaborative discussions. Previous studies showed that the design of learning spaces impacts on students’ achievement. However, acoustic requirements of the active learning classrooms have not been investigated yet. This study aims to estimate activity noise levels by means of unsupervised learning methods, while active learning is practiced in classrooms. Three clustering algorithms, including K-means clustering, Gaussian mixture model, and spectral clustering algorithms, are employed to analyze the continuous one-third octave band sound pressure levels (SPLs). The data were being collected from five active learning classes and two traditional lecture classes at Concordia University in Montreal, Canada. Based on the spectral characteristics of the speech and non-speech signals, and by using the results of previous studies, a unique decision chart is developed in this study in order to assign the activities in to the clusters obtained from the algorithms. Employing the algorithms along with the decision chart, predicts the acoustic levels of assorted class activities such as lecturer's speech, students’ group work and ambient condition. The predicted activities and their corresponding acoustic levels are then compared with the actual results obtained by the researcher during the measurements and the performance of each algorithm is evaluated. Lastly, this study compares the developed method to predict activity noise levels in occupied classrooms with the two other methods proposed in previous studies and the advantages and disadvantages of the developed method are further discussed. The results obtained from employing the Gaussian Mixture Model (GMM) along with the developed decision chart, indicates the best performance among the other methods investigated in this study.