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Workshop on Computational Models of Natural Argument

International Workshop on Empathic Computing

International Workshop on Empathic Computing

CMNA 2015, IWEC 2015, IWEC 2014: Principles and Practice of Multi-Agent Systems pp 129–142Cite as

Identifying Significant Task-Based Predictors of Emotion in Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9935))

Abstract

Emphatic computing is concerned with enabling a system to recognize a user’s current state and then providing the appropriate response to the user with the intention to support the user emotionally. However, in order to do so, the system must first identify the state of the user. Studies in computer-based tutoring are increasingly investigating ways to incorporate synthetic tutors that are equipped with computational models of empathy – in which these agents are trained to understand learners’ emotions and respond based on the detected learner state. However, cultural differences affect the way people express and detect emotions. This paper attempts to identify the task-based features that could discriminate the learner’s emotions in a Malaysian context. By studying several existing task-based features from literature, and combining them with new features, this study attempts to detect four frequent emotions that accompanies learning, namely, frustration, boredom, uncertainty and neutral. A user study is conducted with 33 students and results revealed that certain features can be used as predictors for the abovementioned emotions. Interestingly, results also showed that there is a tendency for students to choose synthetic tutors of the same race.

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References

  1. Davis, M.H.: Empathy: A Social Psychological Approach. Westview Press, Boulder (1994)

    Google Scholar 

  2. Sabourin, J., Mott, B., Lester, J.: Computational models of affect and empathy for pedagogical virtual agents. In: Standards in Emotion Modeling, Lorentz Center International Center for Workshops in the Sciences

    Google Scholar 

  3. Aquino, R.J., Battad, J., Ngo, C.F., Uy, G., Trogo, R., Suarez, M.: Towards empathic support provision for computer users. In: Nishizaki, S.-H., Numao, M., Caro, J., Suarez, M.T. (eds.) Theory and Practice of Computation, vol. 5, pp. 15–27. Springer, Tokyo (2012)

    Chapter  Google Scholar 

  4. D’Mello, S., Picard, R.W., Graesser, A.: Toward an affect-sensitive AutoTutor. IEEE Intell. Syst. 22, 53–61 (2007)

    Article  Google Scholar 

  5. Rajendran, R., Iyer, S., Murthy, S., Wilson, C., Sheard, J.: A theory-driven approach to predict frustration in an ITS. IEEE Trans. Learn. Technol. 6, 378–388 (2013)

    Article  Google Scholar 

  6. Syed Mohamad, S.J.A.N.: Learning Style among Multi-Ethnic Students in Four Selected Tertiary Institutions in the Klang Valley. Universiti Putra Malaysia (2006)

    Google Scholar 

  7. Matsumoto, D., Juang, L.: Culture and Psychology. Cengage Learning, Boston (2012)

    Google Scholar 

  8. Pardos, Z.A., Baker, R.S., San Pedro, M., Gowda, S.M., Gowda, S.M.: Affective states and state tests: investigating how affect and engagement during the school year predict end-of-year learning outcomes. J. Learn. Anal. 1, 107–128 (2014)

    Article  Google Scholar 

  9. Wang, Z., Qiao, X., Xie, Y.: An emotional intelligent e-learning system based on mobile agent technology. In: International Conference on Computer Engineering and Technology, 2009, ICCET 2009, pp. 51–54. IEEE (2011)

    Google Scholar 

  10. Robison, J., McQuiggan, S., Lester, J.: Evaluating the consequences of affective feedback in intelligent tutoring systems. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–6. IEEE (2009)

    Google Scholar 

  11. Rodrigo, M.M.T., Baker, R.S.: Coarse-grained detection of student frustration in an introductory programming course. In: Proceedings of the Fifth International Workshop on Computing Education Research Workshop, pp. 75–80. ACM (2009)

    Google Scholar 

  12. McQuiggan, S.W., Lester, J.C.: Modeling and evaluating empathy in embodied companion agents. Int. J. Hum.-Comput. Stud. 65, 348–360 (2007)

    Article  Google Scholar 

  13. Daradoumis, T., Arguedas, M., Xhafa, F.: Current trends in emotional e-learning: new perspectives for enhancing emotional intelligence. In: 2013 7th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 34–39. IEEE (2013)

    Google Scholar 

  14. D’mello, S.K., Craig, S.D., Witherspoon, A., Mcdaniel, B., Graesser, A.: Automatic detection of learner’s affect from conversational cues. User Model. User-Adap. Interact. 18, 45–80 (2008)

    Article  Google Scholar 

  15. Baker, R.S., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. Int. J. Hum.-Comput. Stud. 68, 223–241 (2010)

    Article  Google Scholar 

  16. Forbes-Riley, K., Litman, D., Friedberg, H., Drummond, J.: Intrinsic and extrinsic evaluation of an automatic user disengagement detector for an uncertainty-adaptive spoken dialogue system. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 91–102. Association for Computational Linguistics (2012)

    Google Scholar 

  17. Plass, J.L., Heidig, S., Hayward, E.O., Homer, B.D., Um, E.: Emotional design in multimedia learning: effects of shape and color on affect and learning. Learn. Instruct. 29, 128–140 (2014)

    Article  Google Scholar 

  18. Forbes-Riley, K., Litman, D.: Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor. Speech Commun. 53, 1115–1136 (2011)

    Article  Google Scholar 

  19. Sabourin, J.L., Lester, J.C.: Affect and engagement in Game-BasedLearning environments. IEEE Trans. Affect. Comput. 5, 45–56 (2014)

    Article  Google Scholar 

  20. McQuiggan, S.W., Robison, J.L., Phillips, R., Lester, J.C.: Modeling parallel and reactive empathy in virtual agents: an inductive approach. In: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 167–174. International Foundation for Autonomous Agents and Multiagent Systems (2008)

    Google Scholar 

  21. Elliott, C.D.: The affective reasoner: a process model of emotions in a multi-agent system. Ph.D. Northwestern University (1992)

    Google Scholar 

  22. Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Cogn. Syst. Res. 5, 269–306 (2004)

    Article  Google Scholar 

  23. Picard, R.W., Picard, R.: Affective Computing. MIT Press, Cambridge (1997)

    Book  Google Scholar 

  24. Graesser, A., McDaniel, B., Chipman, P., Witherspoon, A., D’Mello, S., Gholson, B.: Detection of emotions during learning with AutoTutor. In: Proceedings of the 28th Annual Meetings of the Cognitive Science Society, pp. 285–290. Citeseer (2006)

    Google Scholar 

  25. Kapoor, A., Picard, R.W.: Multimodal affect recognition in learning environments. In: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 677–682. ACM (2005)

    Google Scholar 

  26. Zhao, H., Sun, B., Hu, X., Zhu, X.: The study of emotional education based on virtual reality in e-learning. In: 1st International Conference on Information Science and Engineering (ICISE), pp. 3540–3543. IEEE (2009)

    Google Scholar 

  27. Hu, Y., Zhao, G.: Virtual classroom with intelligent virtual tutor. In: International Conference on e-Education, e-Business, e-Management, and e-Learning, IC4E 2010, pp. 34–38. IEEE (2010)

    Google Scholar 

  28. Chaffar, S., Frasson, C.: Using an emotional intelligent agent to improve the learner’s performance. In: Proceedings of the Workshop on Social and Emotional Intelligence in Learning Environments in Conjunction with Intelligent Tutoring Systems (2004)

    Google Scholar 

  29. Lutfi, S.L., Fernández-Martínez, F., Lorenzo-Trueba, J., Barra-Chicote, R., Montero, J.M.: I feel you: the design and evaluation of a domotic affect-sensitive spoken conversational agent. Sensors 13, 10519–10538 (2013)

    Article  Google Scholar 

  30. Hsiao, I.H., Sosnovsky, S., Brusilovsky, P.: Guiding students to the right questions: adaptive navigation support in an e-learning system for Java programming. J. Comput. Assist. Learn. 26, 270–283 (2010)

    Article  Google Scholar 

  31. Gulzar, S., Yahya, F., Nauman, M., Mir, Z., Mujahid, S.H.: Frustration among University Students in Pakistan (2012)

    Google Scholar 

  32. Blair, C.: School readiness: integrating cognition and emotion in a neurobiological conceptualization of children’s functioning at school entry. Am. Psychol. 57, 111 (2002)

    Article  Google Scholar 

  33. Pekrun, R., Elliot, A.J., Maier, M.A.: Achievement goals and achievement emotions: testing a model of their joint relations with academic performance. J. Educ. Psychol. 101, 115 (2009)

    Article  Google Scholar 

  34. Valiente, C., Swanson, J., Eisenberg, N.: Linking students’ emotions and academic achievement: when and why emotions matter. Child. Dev. Perspect. 6, 129–135 (2012)

    Article  Google Scholar 

  35. Wong, K.Y., Quek, K.S.: Do Chinese and Malay students report different ways of studying mathematics? 1–13 (2007)

    Google Scholar 

  36. Pekrun, R., Goetz, T., Perry, R.P., Kramer, K., Hochstadt, M., Molfenter, S.: Beyond test anxiety: development and validation of the test emotions questionnaire (TEQ). Anxiety Stress Coping 17, 287–316 (2004)

    Article  Google Scholar 

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Acknowledgement

The authors would like to thank Universiti Sains Malaysia for the funding of this work from the grant no. 304/PKOMP/6312153.

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Authors and Affiliations

  1. School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Pulau Pinang, Malaysia

    Najlaa Sadiq Mokhtar & Syaheerah Lebai Lutfi

Authors
  1. Najlaa Sadiq Mokhtar
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  2. Syaheerah Lebai Lutfi
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Corresponding author

Correspondence to Syaheerah Lebai Lutfi .

Editor information

Editors and Affiliations

  1. Università degli Studi di Torino, Turin, Italy

    Matteo Baldoni

  2. Università degli Studi di Torino, Turin, Italy

    Cristina Baroglio

  3. Utrecht University, Utrecht, The Netherlands

    Floris Bex

  4. University of Liverpool, Liverpool, United Kingdom

    Floriana Grasso

  5. University of North Carolina Greensboro, Greensboro, North Carolina, USA

    Nancy Green

  6. University of Wollongong, Wollongong, New South Wales, Australia

    Mohammad-Reza Namazi-Rad

  7. Osaka University, Osaka, Japan

    Masayuki Numao

  8. De La Salle University, Manila, Philippines

    Merlin Teodosia Suarez

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Mokhtar, N.S., Lutfi, S.L. (2016). Identifying Significant Task-Based Predictors of Emotion in Learning. In: Baldoni, M., et al. Principles and Practice of Multi-Agent Systems. CMNA IWEC IWEC 2015 2015 2014. Lecture Notes in Computer Science(), vol 9935. Springer, Cham. https://doi.org/10.1007/978-3-319-46218-9_11

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  • DOI: https://doi.org/10.1007/978-3-319-46218-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46217-2

  • Online ISBN: 978-3-319-46218-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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