  Teaching is one of the most important factors affecting any education system.
Many research efforts have been conducted to facilitate the presentation modes
used by instructors in classrooms as well as provide means for students to
review lectures through web browsers. Other studies have been made to provide
acoustical design recommendations for classrooms like room size and
reverberation times. However, using acoustical features of classrooms as a way
to provide education systems with feedback about the learning process was not
thoroughly investigated in any of these studies. We propose a system that
extracts different sound features of students and instructors, and then uses
machine learning techniques to evaluate the acoustical quality of any learning
environment. We infer conclusions about the students' satisfaction with the
quality of lectures. Using classifiers instead of surveys and other subjective
ways of measures can facilitate and speed such experiments which enables us to
perform them continuously. We believe our system enables education systems to
continuously review and improve their teaching strategies and acoustical
quality of classrooms.
