A Survey of Machine Learning and Deep Learning Methods for Estimating Automatic Engagement and Attention in Offline Classroom Environment

This paper aims to present a literature review in the area of machine learning (ML) and deep learning (DL) methods used in automatic estimation of attention and engagement in offline classroom. A literature search was carried out with relevant set of keywords in Scopus, IEEE, and Proquest databases with a time line of 2010–2023. A total of twenty-four articles were selected to address the aim of the study. The paper highlights the importance of attention and engagement estimation by highlighting the challenges faced in an offline classroom environment. Further, the ML and DL methods are summarized as traditional, contemporary, and other methods. The input dataset along with the performance metrics used to analyze the data in each study is discussed in detail. This study will enable the machine learning experts and researchers to get an insight on the current research in this area and help them to bring robust technological interventions in the field of automatic estimation of attention and engagement for offline classroom environment.