Automated Inattention and Fatigue Detection System in Distance Education for Elementary School Students

Most courses based on distance learning focus on the cognitive domain of learning. Because students are sometimes inattentive or tired, they may neglect the attention goal of learning. This study proposes an autodetection and reinforcement mechanism for the distance-education system based on the reinforcement teaching strategy. If a student is detected to be inattentive or fatigued, then the alert reinforcement feedback window is given; if the attentive time in learning has been reached, then encouraging reinforcement feedback is given. In distance education, the teacher could maintain the learning quality of students through this mechanism. Results of the simulative test and class experiment indicate that this mechanism detects the behavior of students with high accuracy. In other words, it can accurately identify learners who are inattentive or fatigued. The proposed mechanism can urge the inattentive learners to achieve the Attending Teaching goal, according to a comparison of results before and after the experimental class obtained from questionnaires and interviews. However, we also found that the mechanism could not effectively alleviate mental fatigue resulting from physiological exhaustion. Thus, students who feel fatigued must be required to learn in a better mental state to avoid wasting time and learning without satisfying effects. This work presents a detection reinforcement mechanism and teaching procedure designed from the perspective of an Affective Domain Teaching Objectives. The proposed mechanism can accurately detect inattentiveness in students, thus helping teachers of distance education courses to handle the students’ learning conditions.