Abstract
Technology has somewhat challenged typically conservative higher education systems to change and move with the times. Current popular methods of classroom delivery technology or systems include MOOCs, Clickers, learning analytics and dashboard systems. Some systems now form basic building blocks of electronic learning such as Moodle, Blackboard etc. While online learning has taken higher education by storm, its effectiveness is arguable. Interestingly, many researchers believe that the holy grail of technologically enhanced tools in higher education lies in the replication of actual human aspects in the classroom. This paper attempts to address one of the missing but crucial natural elements in the online learning environment – emotion. While most current online teaching and learning systems often process user data through simple decisions using true or false processes i.e. understand or do not understand input, natural human communication is far more complex and rich. Communication involves emotions that can range from bored, excited, interested, confused, distracted, frustrated, angry, etc. In a classroom environment, scanning and assessing student behaviour and expressions enable a teacher to quickly change teaching strategies or pinpoint individuals who need special attention. This paper argues for emotion based online learning systems by discussing the merits of emotion based learning systems, highlighting current key research and advancements in emotion recognition systems, and outlining a proposed methodology and model for the study and implementation of such a system in higher education.
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References
Adolphs, R., Damasio, H., Tranel, D., & Damasio, A. R. (1996). Cortical systems for the recognition of emotion in facial expressions. The Journal of Neuroscience, 16(23), 7678–7687.
Alepis, E., & Virvou, M. (2011). Automatic generation of emotions in tutoring agents for affective e-learning in medical education. Expert Systems with Applications, 38(8), 9840–9847. doi:http://dx.doi.org/10.1016/j.eswa.2011.02.021
Bartlett, M. S., Littlewort, G. C., Frank, M. G., Lainscsek, C., Fasel, I. R., & Movellan, J. R. (2006). Automatic recognition of facial actions in spontaneous expressions. Journal of Multimedia, 1(6), 22–35.
Barton, J. (1996). Interpreting character emotions for literature comprehension. Journal of Adolescent & Adult Literacy, 40(1), 22.
Baylor, A. (2000). Beyond butlers: Intelligent agents as mentors. Journal of Educational Computing Research, 22(4), 373–382.
Ben Ammar, M., Neji, M., Alimi, A. M., & Gouardères, G. (2010). The affective tutoring system. Expert Systems with Applications, 37(4), 3013–3023. doi:http://dx.doi.org/10.1016/j.eswa.2009.09.031
Bovee, C. L., & Thill, J. V. (2010). Business communication today. Upper Saddle River: Pearson.
Buck, R. (2006, March 14–16). The evolutionary bases of social and moral emotions: Dominance, submission, and true love. Paper presented at the 9th Sydney symposium of social psychology, Sydney, Australia.
Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004, November 12–16). Analysis of emotion recognition using facial expressions, speech and multimodal information. Paper presented at the Proceedings of the 6th international conference on multimodal interfaces, Istanbul.
Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241–250.
Dow, J. (2014). Aristotle’s theory of the emotions: Emotions as pleasures and pains. Moral Psychology and Human Action in Aristotle. Retrieved September 5, 2014, from http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199546541.001.0001/acprof-9780199546541-chapter-3
Ekman, P., & Rosenberg, E. L. (1997). What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS). New York: Oxford University Press.
Eveland, C. K., Socolinsky, D. A., & Wolff, L. B. (2003). Tracking human faces in infrared video. Image and Vision Computing, 21(7), 579–590.
Falout, J., Elwood, J., & Hood, M. (2009). Demotivation: Affective states and learning outcomes. System, 37(3), 403–417.
Fragopanagos, N., & Taylor, J. G. (2005). Emotion recognition in human–computer interaction. Neural Networks, 18(4), 389–405.
Hancock, J. T., Landrigan, C., & Silver, C. (2007, April 28–May 3). Expressing emotion in text-based communication. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems, San Jose, California.
Hwang, K.-A., & Yang, C.-H. (2009). Assessment of affective state in distance learning based on image detection by using fuzzy fusion. Knowledge-Based Systems, 22(4), 256–260.
Jones, A., & Issroff, K. (2005). Learning technologies: Affective and social issues in computer-supported collaborative learning. Computers & Education, 44(4), 395–408.
Kim, K. H., Bang, S., & Kim, S. (2004). Emotion recognition system using short-term monitoring of physiological signals. Medical and Biological Engineering and Computing, 42(3), 419–427.
Kort, B., & Reilly, R. (2002). Theories for deep change in affect-sensitive cognitive machines: A constructivist model. Educational Technology & Society, 5(4), 56–63.
Kort, B., Reilly, R., & Picard, R. W. (2001). External representation of learning process and domain knowledge: Affective state as a determinate of its structure and function. In Proceedings of the Artificial Intelligence in Education Workshops AI-ED (pp. 64–69), San Antonio, TX.
Lowendahl, J. M. (2011). Hype cycle for education, 2011. Retrieved October 15, 2014, from https://www.gartner.com/doc/1755718/hype-cycle-education
Lowendahl, J. M. (2014). Hype cycle for education, 2014. Retrieved September 9, 2014, from https://www.gartner.com/doc/2806424?ref=ddisp
O’Regan, K. (2003). Emotion and e-learning. Journal of Asynchronous Learning Networks, 7(3), 78–92.
Park, J., Barash, V., Fink, C., & Cha, M. (2013, June 8–11). Emoticon style: Interpreting differences in emoticons across cultures. Paper presented at the 7th International AAAI conference on weblogs and social media, Ann Arbor, MI.
Qian, R. J., & Matthews, K. E. (2002). Method for robust human face tracking in presence of multiple persons. Google Patents.
Read, J. (2005, June 27). Using emoticons to reduce dependency in machine learning techniques for sentiment classification. Paper presented at the Proceedings of the ACL student research workshop, Ann Arbor, MI.
Rudman, P., & Zajicek, M. (2006, December 18–22). Autonomous agent as helper-helpful or annoying? Paper presented at the IEEE/WIC/ACM International conference on intelligent agent technology, Hong Kong.
Vogt, T., André, E., & Bee, N. (2008). EmoVoice—A framework for online recognition of emotions from voice. Paper presented at the IEEE tutorial and research workshop on Perception in multimodal dialogue systems (pp. 188–199). Springer.
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Kung-Keat, T., Ng, J. (2016). Confused, Bored, Excited? An Emotion Based Approach to the Design of Online Learning Systems. In: Fook, C., Sidhu, G., Narasuman, S., Fong, L., Abdul Rahman, S. (eds) 7th International Conference on University Learning and Teaching (InCULT 2014) Proceedings. Springer, Singapore. https://doi.org/10.1007/978-981-287-664-5_19
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DOI: https://doi.org/10.1007/978-981-287-664-5_19
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