A DEEP LEARNING FRAMEWORK TO EVALUATE TEACHER CONFIDENCE IN THE CLASSROOM

The need to make the current assessment system more effective, stable, and amenable to quantitative analysis is clearly felt. The teacher's confidence in the chosen assessment method largely determines the quality of the student's learning experience. In this context, we have implemented an automated analysis framework based on convolutional neural networks to measure the level of teacher confidence, which is able to effectively overcome the inherent limitations of traditional human-based techniques. This model reveals an information-based and unbiased assessment that significantly reduces errors arising from personal interpretations or evaluative bias. This system also works nicely when processing a large-scale assessment data, which significantly strengthens overall scalability. Third, it provides real-time actionable feedback to instructors, allowing them to adapt and optimize lesson plans instantly. Additionally, this system generates data-driven insights for person-centered support and professional development and ensures the continuity of the assessment process. The proposed research presents a deep convolutional neural network-based method for image classification of various non-verbal cues including posture, makeup, etc. This system helps target-oriented interventionists improve the quality of education and the classroom learning environment, and provides specific results after effective implementation. The model achieved 89.33% training accuracy, 0.89 recall, 0.89 precision, and 0.89 F1-score in human gesture recognition through a deep learning-based approach.