TY - GEN
T1 - Measuring academic emotions and facial expressions in online video-based learning
AU - Lee, Jihyang
AU - Park, Hyunjin
AU - So, Hyo Jeong
N1 - Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A5A8030048).
Publisher Copyright:
© 2018 Asia-Pacific Society for Computers in Education..All Rights Reserved.
PY - 2018/11/24
Y1 - 2018/11/24
N2 - This study proposes the new classification of analyzing academic emotions and facial expressions measured in online video-based learning contexts. To this end, we conducted a qualitative single-subject research on a learner to unpack the learner’s academic emotions and facial expressions revealed during the online learning process. Drawn from the review of relevant literature, seven types of emotions were classified as positive or negative. The types of positive emotions captured are: 1) excitement, enjoyment, and pleasure 2) confidence, and 3) aspiration, enthusiasm and expectation. The types of negative emotions are: 1) fear and anxiety, 2) embarrassment and shame, 3) frustration and alienation, and 4) boredom. Recall interview and observation were also used to infer the facial expressions matched to each emotion type, which were measured through the movements of eyes, eyebrows, lip, and jaw. Overall, 3 to 4 facial expressions were revealed per emotion. Based on the results, we propose a new scheme to classify academic emotions in online video-based learning, and suggest some areas for future research on utilizing affective computing technology in academic emotions, and appropriating emotional support in online learning.
AB - This study proposes the new classification of analyzing academic emotions and facial expressions measured in online video-based learning contexts. To this end, we conducted a qualitative single-subject research on a learner to unpack the learner’s academic emotions and facial expressions revealed during the online learning process. Drawn from the review of relevant literature, seven types of emotions were classified as positive or negative. The types of positive emotions captured are: 1) excitement, enjoyment, and pleasure 2) confidence, and 3) aspiration, enthusiasm and expectation. The types of negative emotions are: 1) fear and anxiety, 2) embarrassment and shame, 3) frustration and alienation, and 4) boredom. Recall interview and observation were also used to infer the facial expressions matched to each emotion type, which were measured through the movements of eyes, eyebrows, lip, and jaw. Overall, 3 to 4 facial expressions were revealed per emotion. Based on the results, we propose a new scheme to classify academic emotions in online video-based learning, and suggest some areas for future research on utilizing affective computing technology in academic emotions, and appropriating emotional support in online learning.
KW - Academic emotion
KW - Affective computing
KW - Facial expression
KW - Online learning
UR - http://www.scopus.com/inward/record.url?scp=85060039000&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85060039000
T3 - ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings
SP - 50
EP - 58
BT - ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings
A2 - Wong, Lung-Hsiang
A2 - Banawan, Michelle
A2 - Srisawasdi, Niwat
A2 - Yang, Jie Chi
A2 - Rodrigo, Ma. Mercedes T.
A2 - Chang, Maiga
A2 - Wu, Ying-Tien
PB - Asia-Pacific Society for Computers in Education
Y2 - 26 November 2018 through 30 November 2018
ER -