Assessment of affective state in distance learning based on image detection by using fuzzy fusion

Distance learning can solve the limitations of time and space in learning. However, due to the distance, teachers cannot manage students learning behaviors, i.e. they do not know whether a student is attentive, drowsy or absent. Teachers can overcome difficulties in students’ management by knowing the affective states of the students. This study adopts image recognition to capture face images of students when they are learning, and analyzes their face features to evaluate their affective states by fuzzy integrals. Test results indicate that the bad affective states are accurately identified. Teachers can monitor the students’ affective states from the detection results on the system interface.