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As synchronous online classrooms have grown more common in recent years, evaluating a student's attention level has become increasingly important in verifying every student's progress in an online classroom setting. This paper describes a study that used machine learning models to monitor student attentiveness to distinct gradients of engagement level. Initially, the experiments were conducted using a deep convolutional neural network of student attention and emotions exploiting Keras library. The model showed a 90% accuracy in predicting attention level of the student. This deep convolutional neural network analysis aids in identifying crucial emotions that are important in determining various levels of involvement. This study discovered that emotions such as calm, happiness, surprise, and fear are important in determining a student's attention level. These findings aided in the earlier discovery of students with poor attention levels, allowing instructors to focus their assistance and advice on the students who require it, resulting in a better online learning environment. |
