research-article

Barrier-free affective communication in MOOC study by analyzing pupil diameter variation

Authors:

Ruimin LyuAuthors Info & Claims

SA '16: SIGGRAPH ASIA 2016 Symposium on Education

Article No.: 7, Pages 1 - 8

https://doi.org/10.1145/2993352.2993362

Published: 28 November 2016 Publication History

Abstract

A MOOC (Massive Open Online Course) study shortens the distance between students and educators, and surpasses time and space, but it also creates barriers of true emotion interaction in the study process. This research demonstrated the feasibility of using a pupil diameter variation as the indicator of implicit affection states for the scales of valence and arousal, aiming to find a way of revealing the students' true emotion. In the experiment, affective music clips were selected as stimuli, the participants' pupillary responses information and their valence-arousal labeling scores were used for emotion recognition modeling. Multilayer Perceptron, Kstar and SVM algorithms were validated in the experiment for model comparison. SVM achieved the best recognition rate in both valence and arousal affective dimensions, indicating that affective states could be recognized by pupil diameter variation. On the basis of the recognition model, an application named "EMOOC" was developed for communication improvement in the MOOC's study, which could visualize students' emotional states to the MOOC teacher as feedback, bridging the emotion gap due to the online communication barrier.

References

[1]

Abubakr, B., Ibrahima, F., and Anuj, M. 2014. Differentiation of pupillary signals using statistical and functional analysis. In International Conference on Intelligent and Advanced Systems, 1--6.

[2]

Al-Omar, D., Areej, A.-W., and Manar, H. 2013. Using pupil size variation during visual emotional stimulation in measuring affective states of non communicative individuals. In HCI International 2013, Part II:UNIVERSAL ACCESS IN HUMAN-COMPUTER INTERACTION, vol. 8010, 253--258.

Digital Library

[3]

Anton, A., Angel, B., Eduardo, B., and Ferran, B. 2015. Affective modulation of the startle reflex and the reinforcement sensitivity theory of personality: The role of sensitivity to reward. Physiology 138, 332--339.

[4]

Ashkan, Y., Jong-Seok, L., Jean-Marc, V., and Touradj, E. 2012. Affect recognition based on physiological changes during the watching of music videos. IEEE Transactions on Interactive Intelligent Systems 2, 1, 7--33.

Digital Library

[5]

Chen, S., and Epps, J. 2012. Automatic classification of eye activity for cognitive load measurement with emotion interference. Computer Methods and Programs in Biomedicine 110, 111C124.

Digital Library

[6]

Chen, W., Er, M., and Wu, S. 2006. Illumination compensation and normalization for robust face recognition using discrete cosine transform in alogarithm domain. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36, 2, 458--466.

Digital Library

[7]

Cheng, B., and Liu, G. 2008. Emotion recognition from surface emg siganl using wavelet transform and neural network. In International Conference on Bioinformatics and Biomedical Engineering, 1363--1366.

[8]

Ekman, P. 1992. An argument for basic emotions. Cognition and Emotion 6, 169--200.

[9]

Fan, R. E., Chen, P. H., and Lin, C. J. 2005. Working set selection using second order information for training svm. Journal of Machine Learning Research 6, 1889--1918.

Digital Library

[10]

Gao, Y., Barreto, A., and Adjouadi, M. 2009. Comparative analysis of noninvasively monitored biosignals for affective assessment of a computer user. In Southern Biomedical Engineering Conference, 255--260.

[11]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. 2009. The weka data mining software: An update. SIGKDD Explorations 11.

Digital Library

[12]

Head, K. 2013. Massive open online adventure. Chronicle of Higher Education 59, 34, 24--25.

[13]

Hevner, K. 1936. Experimental studies of the elements of expression in music. American Journal of Psychology 48, 246--268.

[14]

Hew, K. F., and Cheung, W. S. 2014. Students and instructors use of massive open online courses (moocs): Motivations and challenges. Educational Research Review 12, 45--48.

[15]

Kawai, S., Takano, H., and Nakamura, K. 2013. Pupil diameter variation in positive and negative emotions with visual stimulus. In IEEE International Conference on Systems, Man and Cybernetics, 4179--4183.

Digital Library

[16]

Kim, J., and Andre, E. 2008. Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 12, 2067--2083.

Digital Library

[17]

Koelstra, S., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., and Patras, I. Y. 2012. Deap: A database for emotion analysis using physiological signals. IEEE Transactions on Affective Computing 3, 1, 18--31.

Digital Library

[18]

Koji, K., Momoyo, I., and Minoru, F. 2011. Development of automatic filtering system for individually unpleasant data detected by pupil-size change. In International Conference on Systems, Man, and Cybernetics, 3311--3316.

[19]

Kolowich, S. 2013. The professors who make the moocs. Chronicle of Higher Education 59, 28, 20--23.

[20]

Lanat, A., Armato, A., Valenza, G., and Scilingo, E. P. 2011. Eye tracking and pupil size variation as response to affective stimuli: a preliminary study. In International Conference on Pervasive Computing Technologies for Healthcare, 78--84.

[21]

Laukka, J. S., Haapala, M., Lehtihalmes, M., Väyrynen, E., and Seppänen, T. 2013. Pupil size variation related to oral report of affective pictures. Procedia-Social and Behavioral Sciences 84, 1, 18--23.

[22]

Masek, L. 2003. Recognition of human iris patterns for biometric identification. In Report for Bachelor of Engineering degree.

[23]

Masheal, A., and Sharifa, A. 2013. An exploratory study of detecting emotion states using eye-tracking technology. In Science and Information Conference, 428--433.

[24]

Miccoli, B. M. M., Escrig, L., Lang, M. A., and Peter, J. 2008. The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45, 4, 602--607.

[25]

Onorati, F., Barbieri, R., Mauri, M., Russo, V., and Mainardi, L. 2013. Reconstruction and analysis of the pupil dilation signal: Application to a psychophysiological affective protocol. Engineering in Medicine and Biology Society 59, 1, 185--198.

[26]

Panksepp, J. 1998. Affective neuroscience: The foundation of human and animal emotions. Oxford University Press.

[27]

Peng, R., Armando, B., Ying, G., and Malek, A. 2011. Affective assessment of computer users based on processing the pupil diameter signal. In International Conference of the Engineering in Medicine and Biology Society, 2594--2597.

[28]

Peng, R., Ana, B., Ying, G., and Malek, A. 2013. Affective assessment by digital processing of the pupil diameter. IEEE Transactions on Affective Computing 4, 1, 2--14.

Digital Library

[29]

Prehn, K., Heekeren, R. H., and der Meer, E. V. 2011. Influence of affective significance on different levels of processing using pupil dilation in an analogical reasoning task. International Journal of Psychophysiology 79, 2, 236--243.

[30]

Raluca, B., Radu, D., and Laura, F. 2013. A comparison of several classifiers for eye detection on emotion expressing faces. In International Symposium on Electrical and Electronics Engineering, vol. 8010, 1--6.

[31]

Roth, M. S. 2013. My modern experience teaching a mooc. Chronicle of Higher Education 59, 34, 18--21.

[32]

Russell, J. 1980. A circumplex model of affect. Personality and Social Psychology 39, 6, 1161--1178.

[33]

Russell, J. 2010. A comparison of the discrete and dimensional models of emotion in music. Psychology of Music 39, 1, 18--49.

[34]

Saromporn, C., and Michiko, O. 2014. Exploring emotion in an e-learning system using eye tracking. In Symposium on Computational Intelligence in Healthcare and e-health, 141--147.

[35]

Xing, B., Zhang, K., Sun, S., Zhang, L., Gao, Z., Wang, J., and Chen, S. 2015. Emotion-driven chinese folk music-image retrieval based on de-svm. Neurocomputing 148, 619--627.

[36]

Zhang, K., and Sun, S. 2013. Web music emotion recognition based on higher effective gene expression programming. Neuro-computing 105, 3, 100--106.

Digital Library

[37]

Zhu, X. 2010. Emotion recognition of emg based on bp neural network. In International Symposium on Networks and Network Security, 227--229.

[38]

Zhua, Z., and Jib, Q. 2005. Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Computer Vision and Image Understanding 98, 124--154.

Digital Library

Cited By

Yang KTag BWang CGu YSarsenbayeva ZDingler TWadley GGoncalves J(2023)Survey on Emotion Sensing Using Mobile DevicesIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322048414:4(2678-2696)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TAFFC.2022.3220484
Moge CWang KCho Y(2022)Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCIProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517495(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517495
Liapis AMaratou VPanagiotakopoulos TKatsanos CKameas A(2022)UX evaluation of open MOOC platforms: a comparative study between Moodle and Open edX combining user interaction metrics and wearable biosensorsInteractive Learning Environments10.1080/10494820.2022.204867431:10(6841-6855)Online publication date: 9-Mar-2022
https://doi.org/10.1080/10494820.2022.2048674
Show More Cited By

Index Terms

Barrier-free affective communication in MOOC study by analyzing pupil diameter variation
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Pupil Diameter Variation in Positive and Negative Emotions with Visual Stimulus
SMC '13: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics

The severely disabled people, e.g. ALS (Amyotrophic Lateral Sclerosis), cannot move their head and hands and desire to communicate with other people. They often feel discomfort because they cannot indicate their intention. To respond their request, the ...
Pupil size variation as an indication of affective processing
Application of affective computing in humanComputer interaction

The present objective was to investigate pupil size variation during and after auditory emotional stimulation. Thirty subjects' (15 females and 15 males) pupil responses were measured while listening to 10 negative and 10 positive highly arousing sounds ...
Dynamics of Emotional States and Their Relationship with Learning Outcomes during Learning Python with MOOC
ICIET 2019: Proceedings of the 2019 7th International Conference on Information and Education Technology

Understanding the relationship between emotion and learning helps to improve the learning experience and learning performance. In order to examine the dynamics of emotional states and their relationship with learning outcomes during learning Python, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SA '16: SIGGRAPH ASIA 2016 Symposium on Education

November 2016

95 pages

ISBN:9781450345446

DOI:10.1145/2993352

Conference Chairs:
Miho Aoki
University of Alaska Fairbanks
,
Zhigeng Pan
Hangzhou Normal University, China

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Natural Science Foundation of China
Foundation of Zhejiang Educational Committee
Department of Science and Technology of Zhejiang Province
Natural Science Foundation of Jiangsu Province

Conference

SA '16

Sponsor:

SIGGRAPH

SA '16: SIGGRAPH Asia 2016

December 5 - 8, 2016

Macau

Acceptance Rates

Overall Acceptance Rate 178 of 869 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
211
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang KTag BWang CGu YSarsenbayeva ZDingler TWadley GGoncalves J(2023)Survey on Emotion Sensing Using Mobile DevicesIEEE Transactions on Affective Computing10.1109/TAFFC.2022.322048414:4(2678-2696)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TAFFC.2022.3220484
Moge CWang KCho Y(2022)Shared User Interfaces of Physiological Data: Systematic Review of Social Biofeedback Systems and Contexts in HCIProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517495(1-16)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3517495
Liapis AMaratou VPanagiotakopoulos TKatsanos CKameas A(2022)UX evaluation of open MOOC platforms: a comparative study between Moodle and Open edX combining user interaction metrics and wearable biosensorsInteractive Learning Environments10.1080/10494820.2022.204867431:10(6841-6855)Online publication date: 9-Mar-2022
https://doi.org/10.1080/10494820.2022.2048674
Mejbri NEssalmi FJemni MAlyoubi B(2022)Trends in the use of affective computing in e-learning environmentsEducation and Information Technologies10.1007/s10639-021-10769-927:3(3867-3889)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10639-021-10769-9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents