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Affective Computing in E-Learning Modules: Comparative Analysis With Two Activities


Maharjan, M and Yeom, SJ and Kim, S-Y and Fan, S, Affective Computing in E-Learning Modules: Comparative Analysis With Two Activities, Interactivity and the Future of the Human-Computer Interface, IGI Global, P Isaias and K Blashki (ed), United States ISBN 9781799826378 (2020) [Research Book Chapter]

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Copyright 2020 IGI Global

DOI: doi:10.4018/978-1-7998-2637-8.ch009


This paper presents a study on emotion of students and reaction towards learning and watching video clip with different personality traits with the help of existing facial expression analyzing applications. To demonstrate this, the userís expressions are recorded as video while watching the movie trailer and doing the quiz. The results obtained are studied to find which emotion is most prevalent among the users in different situations. This study shows that students experience seemingly different emotions during the activity. This study explores the use of affective computing for further comprehension of studentsí emotion in learning environments. While previous studies show that there is a positive correlation between emotion and academics, the current study demonstrated the existence of the inverse relation between them. In addition, the study of the facial analysis of movie trailer confirmed that different people have different ways of expressing the feeling. Results of the study will help to further clarify connection between various personality traits and emotion.

Item Details

Item Type:Research Book Chapter
Keywords:affective computing, emotion, learning activity
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial life and complex adaptive systems
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Maharjan, M (Mrs Mahima Maharjan)
UTAS Author:Yeom, SJ (Dr Soonja Yeom)
UTAS Author:Fan, S (Dr Frances Fan)
ID Code:137611
Year Published:2020
Deposited By:Information and Communication Technology
Deposited On:2020-02-22
Last Modified:2020-05-18

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