User Emotion Status Recognition in MOOCs Study Environment Based on Eye Tracking and Video Feature Fusion

Emotion is an essential aspect that influences the learning outcome and MOOCs learning experience. This research investigates the possibility of employing the fusion characteristics of video and users' eye tracking features to assess MOOC learners' emotions. In the experiment, we gathered eye tracking data from MOOCs students. We applied OpenSmile and OpenCV to extract the video features. And a fusion data set with the eye-tracking data and video features was established in the experiment. Machine learning method is utilized to explore the optimal model using fused feature data sets, and the impacts of multimodal features on emotion recognition are discussed. Our findings demonstrate that the fusion feature can achieve an accuracy rate of 86.3%, which outperformed the single-modal feature sets. According to the study, this technique can be used to learn students' emotion status in their MOOC study and predict the students' affective perception on MOOCs videos.