An IoT-Enabled Machine Learning Framework for Automated Teacher Performance Feedback to Enhance Teaching Quality

Given the crucial role of teachers in the education system, robust mechanisms are necessary to enhance their teaching effectiveness. Through leveraging advanced technological methods, both teacher and student evaluation processes can be conducted with high accuracy. This study proposes an IoT-based automated teacher performance evaluation system that utilizes machine learning algorithms and computer vision techniques to provide immediate feedback on teaching performance to supervisors. The system analyzes key elements such as hand movements and the teacher's position in the classroom. By enhancing teaching performance, the model aims to improve student learning outcomes. In addition, to develop and test the system, a hypothetical dataset - called the teacher dataset - was created for this proposed model by collecting 35 publicly available videos from YouTube. This approach employs a ResNet50 pre-trained neural network for transfer learning and feature extraction to classify teacher behavior into 8 classes. Fuzzy logic converts the predictions into three teaching quality ratings (poor/medium/good). Using this custom dataset, the model achieved an accuracy of 84.8%, indicating strong performance. This approach enables automated feedback on teaching style, reducing the need for in-person evaluations by educational supervisors. The proposed system has the potential to significantly enhance the overall quality of teaching and learning.