PL EN


Journal
2017 | 3 (70) | 15-24
Article title

Investigation of educational processes with affective computing methods

Content
Title variants
Languages of publication
EN
Abstracts
EN
This paper concerns the monitoring of educational processes with the use of new technologies for the recognition of human emotions. This paper summarizes results from three experiments, aimed at the validation of applying emotion recognition to e-learning. An analysis of the experiments' executions provides an evaluation of the emotion elicitation methods used to monitor learners. The comparison of affect recognition algorithms was based on the criteria of availability, accuracy, robustness to disturbance, and interference with the e-learning process. The lessons learned in these experiments might be of interest to teachers and e-learning tutors, as well as to those researchers who want to use affective computing methods in monitoring educational processes.
Keywords
Journal
Year
Issue
Pages
15-24
Physical description
Dates
published
2017
Contributors
References
  • Ang, J., Dhillon, R., Krupski, A., Shriberg, E., & Stolcke, A. (2002). Prosody-based automatic detection of annoyance and frustration in human-computer dialog. Proceedings of the 7th International Conference on Spoken Language Processing (ICSLP 2002), 2037-2040.
  • Bailenson, J.N., Pontikakis, E.D., Mauss, I.B., Gross, J.J., Jabon, M.E., Hutcherson, C.A.C, & John, O. (2008). Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-Computer Studies, 66(5), 303-317. http://dx.doi.org/10.1016/j.ijhcs.2007.10.011
  • Baker, R.S.J. d. (2007). Modeling and understanding students' off-task behavior in intelligent tutoring systems. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1059.
  • Ben Ammar, M., Neji, M., Alimi, A.M., & Gouarderes, G. (2010). The affective tutoring system. Expert Systems with Applications, 37(4), 3013-3023. http://dx.doi.org/10.1016/j.eswa.2009.09.031
  • Bessiere, K., Newhagen, J.E., Robinson, J.P., & Shneiderman, B. (2006). A model for computer frustration: The role of instrumental and dispositional factors on incident, session, and post-session frustration and mood. Computers in Human Behavior, 22(6), 941-961.
  • Binali, H., Wu, C., & Potdar, V. (2009), A new significant area: Emotion detection in e-learning using opinion mining techniques. 3rd IEEE International Conference on Digital Ecosystems and Technologies, 259-264. http://dx.doi.org/10.1109/DEST.2009.5276726
  • Binali, H., Wu, C., & Potdar, V. (2010). Computational approaches for emotion detection in text. 4th IEEE International Conference on Digital Ecosystems and Technologies - IEEE DEST, 172-177.
  • Boehner, K., Depaula, R., Dourish, P., & Sengers, P. (2007). How emotion is made and measured. International Journal of Human-Computer Studies, 65(4), 275-291. http://dx.doi.org/10.1016/j.ijhcs.2006.11.016
  • Elliott, C., Rickel, J., & Lester, J.C. (1999). Lifelike pedagogical agents and affective computing: An exploratory synthesis. Lecture notes on artificial intelligence. Artificial Intelligence Today, 1600, 195-211.
  • Goleman, D. (2006). Emotional intelligence. USA: Bantam.
  • Gunes, H., & Schuller, B. (2013). Categorical and dimensional affect analysis in continuous input: Current trends and future directions. Image and Vision Computing, 31(2), 120-136. http://dx.doi.org/10.1016/j.imavis.2012.06.016
  • Hone, K. (2006). Empathic agents to reduce user frustration: The effects of varying agent characteristics. Interacting with Computers 18(2), 227-245. http://dx.doi.org/10.1016/j.intcom.2005.05.003
  • Hudlicka, E. (2003). To feel or not to feel: The role of affect in human-computer interaction. The International Journal of Human-Computer Studies, 59(1-2), 1-32. http://dx.doi.org/10.1016/S1071-5819(03)00047-8
  • Kapoor, A., Mota, S., & Picard, R.W. (2001). Towards a learning companion that recognizes affect. Association for Advancement of Artificial Intelligence Fall Symposium, 543, 2-4.
  • Kołakowska, A. (2013). A review of emotion recognition methods based on keystroke dynamics and mouse movements. 6th International Conference on Human System Interactions. http://dx.doi.org/10.1109/HSI.2013.6577879
  • Landowska, A. (2013). Affective computing and affective learning - methods, tools and prospects. EduAkcja. Magazyn edukacji elektronicznej, 1(5), 16-31.
  • Landowska, A. (2015a). Emotion monitor - Concept, construction and lessons learned. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, 75-80.
  • Landowska, A. (2015b). Towards emotion acquisition in IT usability evaluation context. Proceedings of the Mulitimedia, Interaction, Design and Innnovation on ZZZ - MIDI, 1-9.
  • Landowska, A. (2016). How to design affect-aware educational systems-the AFFINT process approach. Proceedings on the European Conference of e-Learning 2016.
  • Landowska, A., & Brodny, G. (2017). Postrzeganie inwazyjności automatycznego rozpoznawania emocji w kontekście edukacyjnym. EduAkcja. Magazyn edukacji elektronicznej, 1(13), 26-41.
  • Landowska, A., Brodny, G., & Wróbel, M.R. (2017). Limitations of emotion recognition from facial expressions in e-learning context. 9th International Conference on Computer Supported Education, 383-389. http://dx.doi.org/10.5220/0006357903830389
  • Landowska, A., & Miler, J. (2016). Limitations of emotion recognition in software user experience evaluation context. Federated Conference on Computer Science and Information Systems, 1631-1640.
  • Li, J., & Ren, F. (2008). Emotion recognition from blog articles. International Conference on Natural Language Processing and Knowledge Engineering, IEEE NLP-KE.
  • Ling, H. S., Bali, R., & Salam R.A. (2006). Emotion detection using keywords spotting and semantic network. Paper presented at the International Conference on Computing and Informatics ICOCI.
  • Maria, K.A., & Zitar, R.A. (2007). Emotional agents: A modeling and an application. Information and Software Technology, 49(7), 695-716, http://dx.doi.org/10.1016/j.infsof.2006.08.002
  • Mehrabian, A. (1996). Pleasure-arousal. Dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology, 14(4), 261-292.
  • Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2009). Compositionality principle in recognition of fine-grained emotions from text. International Association for Advancement of Artificial Intelligence Conference on Web and Social Media, 278-281.
  • Paiva, A., Dias, J., Sobral, D., & Woods, S. (2004). Building empathic lifelike characters: the proximity factor. International Conference on Autonomous Agents and Multiagent Systems, 4.
  • Picard, R.W. (2003). Affective computing: challenges. The International Journal of Human-Computer Studies, 59(1-2), 55-64. http://dx.doi.org/10.1016/S1071-5819(03)00052-1
  • Picard, R.W., & Ahn, H. (2006). Affective cognitive learning and decision making: The role of emotions. Lecture Notes in Computer Science book series, 3784, 866-873.
  • Picard, R.W., & Daily, S. (2005). Evaluating affective interactions: Alternatives to asking what users feel. Proceedings CHI'05 Workshop on Evaluating Affective Interfaces: Innovative Approaches.
  • Picard, R.W., & Klein, J. (2002). Computers that recognise and respond to user emotion: theoretical and practical implications. Interacting with Computers, 14(2), 141-169. http://dx.doi.org/10.1016/S0953-5438(01)00055-8
  • Scheirer, J., Fernandez, R., Klein, J., & Picard, R.W. (2002). Frustrating the user on purpose: a step toward building an affective computer. Interacting with Computers, 14(2), 93-118. http://dx.doi.org/10.1016/S0953-5438(01)00059-5
  • Scherer, K.R., & Ekman, P. (1984). Approaches to emotion. Hillsdale, NJ: L. Erlbaum Associates.
  • Sheng, Z., Zhu-ying, L., & Wan-xin, D. (2010). The model of E-learning based on affective computing. 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 3, 269-272.
  • Strapparava, C., & Valitutti, A. (2004). WordNet-Affect: an affective extension of WordNet. 4th International Conference on Language Resources and Evaluation, 1083-1086.
  • Wioleta, S. (2013). Using physiological signals for emotion recognition. The 6th International Conference on Human System Interaction, 556-561.
  • Woolf, B., Burleson, W., & Arroyo, I. (2009). Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology, 4(3/4), 129-164. http://dx.doi.org/10.1504/IJLT.2009.028804
  • Yik, M.S. M., Russell, J.A., & Barrett, L.F. (1999). Structure of self-reported current affect: Integration and beyond. Journal of Personality and Social Psychology, 77(3), 600-619. http://dx.doi.org/10.1037/0022-3514.77.3.600
  • Zeng, Z., Pantic, M., Roisman, G.I., & Huang, T. S. (2007). A survey of affect recognition methods. Proceedings of the 9th International Conference on Multimodal Interfaces. Paper presented at ICMI 2007 in Nagoya, Japan, November 12-15 (pp. 126-133). http://dx.doi.org/10.1145/1322192.1322216
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-36eb41d4-a468-4724-9666-9578e484260c
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.