Assessment of Learners' Attention to E-Learning by Monitoring Facial Expressions for Computer Network Courses

Abstract

Recognition of students' facial expressions can be used to understand their level of attention. In a traditional classroom setting, teachers guide the classes and continuously monitor and engage the students to evaluate their understanding and progress. Given the current popularity of e-learning environments, it has become important to assess the degree of attention during the online learning process. In this study, we used interactive video-capture facial-recognition technology to automatically detect the facial expressions of students as a means of analyzing their attention state during the e-learning process. Participants were divided into three different learning-strategy groups for a course on computer networks. An attention-detection feedback module evaluated participants' attention span during the learning sessions and initiated a response to redirect the participants' attention when they became distracted. The three groups of participants showed significant differences in their course achievement; this was attributed to the different learning strategies used for content presentation. A positive correlation was found between learning improvement and attention, indicating that video-capture facial-recognition technology can be used to provide timely learning assistance and appropriate stimulation to enhance the educational benefits of e-learning.