System for recognizing lecture quality based on analysis of physical parameters

In this paper we have presented a smart classroom system that is able to classify students' satisfaction with the lecture quality by examining parameters of the physical environment obtained using different smart devices. The system is based on the Random forest classifier, which showed the best accuracy among all machine learning algorithms available in Weka tool, with dataset collected during 28 lectures and evaluated using 10-fold cross validation. The system is implemented using different set of tools (such as Matlab and Weka) and can extract features from the ambient sound and analyze values obtained from different smart devices deployed in the classroom. Based on the extracted and captured data the system provides in real time information about the students' satisfaction with the lecture quality. For the validation purposes, we recorded 13 more lectures attended by four different student groups where the number of students varied from 5 to 18. The system accuracy was evaluated by comparing system outputs with the students' feedback and ranged from 70.7% to 83.9%.