Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques

Emotional Design Tutoring System Based on Multimodal Affective Computing Techniques

Cheng-Hung Wang (National University of Kaohsiung, Kaohsiung, Taiwan) and Hao-Chiang Koong Lin (National University of Tainan, Tainan, Taiwan)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJDET.2018010106
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In a traditional class, the role of the teacher is to teach and that of the students is to learn. However, the constant and rapid technological advancements have transformed education in numerous ways. For instance, in addition to traditional, face to face teaching, E-learning is now possible. Nevertheless, face to face teaching is unavailable in distance education, preventing the teacher from understanding the student's learning emotions and states; hence, a system can be adopted to collect information on students' learning emotions, thereby compiling data to analyze their learning progresses. Hence, this study established an emotional design tutoring system (EDTS) and investigated whether this system influences user interaction satisfaction and elevates learning motivation. This study determined that the learners' perception of affective tutoring systems fostered positive attitudes toward learning and thereby promoted learning effects. The experimental results offer teachers and learners an efficient technique for boosting students' learning effects and learning satisfaction. In the future, affective computing is expected to be widely used in teaching. This can enable students to enjoy learning in a multilearning environment; thus, they can exhibit higher learning satisfaction and gain considerable learning effects.
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Literature Review

Affective computing has been gradually progressing. Numerous domestic and international scholars have employed various mediums to probe human interactions in their studies on emotion recognition. Various sensors can detect facial expressions and physiological signals aroused by emotions and feelings. Subsequently, these signals are interpreted as people’s feelings, and appropriate feedback is then provided (Clavel & Callejas, 2016; Manovich, 2001; Vesterinen, 2001; Wan, 2007). Extensive research is being conducted in this field to determine how to set up emotional perception, develop proper emotion models, express emotions appropriately in different ways, and even transmit emotions on the Internet.

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