Anonymized In-classroom-Interaction-Detection Dataset

Teacher performance evaluation is crucial for enhancing teaching quality and supporting professional development. Traditional methods often suffer from subjectivity, time-consuming processes, and limited reliability. In this study, we present an AI-powered framework for Assessing Teacher Performance in Classroom Interactions using deep learning techniques. Our framework employs three state-of-the-art object detection algorithms: YOLOv8, Faster R-CNN, and RetinaNet, to detect and analyze eleven in-classroom interactions. A labeled dataset of 7259 images collected from actual classrooms was used to train and evaluate the models. This anonymized labeled image dataset has been created to capture various teacher and student interactions and behaviors within a classroom environment. The dataset includes detailed annotations for activities such as **Closed-Book, Electronic-Book, No-Book, Opened-Book, Raising-Hand, Student-Answers, Student-Reads, Student-Writes, Teacher-Explains, Teacher-Follows-up-Students, and Worksheet**, providing a comprehensive representation of classroom dynamics.