Comparative study of dataset for classroom Attention Analysis

The quality and appropriateness of the datasets used for training and assessment have a significant impact on how well artificial intelligence and deep learning models perform in classroom attention analysis. With an emphasis on kids and educational environments, this study offers a comparative examination of publicly accessible and research-focused datasets used for classroom attention and behavior analysis. The review examines datasets according to their application to attention state recognition, subject demographics, annotation techniques, data modalities, and ethical considerations. This study attempts to assist academics in choosing suitable datasets without disclosing secret algorithms by methodically analyzing datasets published in recent publications. Additionally, the review draws attention to current shortcomings and difficulties, encouraging the creation of new datasets for reliable classroom video analysis.