1. Research on Emotional State in Online Learning by Eye Tracking Technology
- Author
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Xiaomei Tao, Qiong Gui, and Shengxi Liu
- Subjects
Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Eye movement ,Cognition ,02 engineering and technology ,Pearson product-moment correlation coefficient ,Correlation ,Stimulus (psychology) ,symbols.namesake ,InformationSystems_MODELSANDPRINCIPLES ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Eye tracking ,020201 artificial intelligence & image processing ,State (computer science) ,Affective computing ,Cognitive psychology - Abstract
In recent years, the intelligence level of eye tracker is increasing, and the application of eye-tracking technology in the field of multimedia learning research is also increasing. At the same time, many related studies have proved the validity of eye movement information in human emotion detection and understanding. The purpose of this paper is to explore the potential psychological state of learners in online learning using eye-tracking technology in multimedia environment, and then by linking the emotional state of learners with related cognitive processing process, to find the relationship between eye movement information and emotional state. This paper uses online learning videos as stimulus materials for research and a total of 38 subjects participated in the experiment. During the period of watching the video material, the eye movement information was recorded by eye tracker. Though the Pearson correlation coefficient analysis method, it is found that in the process of online learning, there is a high correlation between emotional state and eye movement information. The pupil diameter of the subjects in the negative emotional state is larger than that in the positive emotional state, and the blink frequency of the subjects in the positive emotional state is higher than that in the negative emotional state. This experiment shows the feasibility of using eye movement information to detect different emotions in the learning process.
- Published
- 2019