A Computational Model to Determine Desirability of Events Based on Personality for Performance Motivational Orientation Learners

  • Somayeh Fatahi
  • Hadi MoradiEmail author
  • Ali Nouri Zonoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9192)


One of the most important discussions in artificial intelligence is the modeling of human behaviors in virtual environments. The factors such as personality, emotion, and mood are important to model human behaviors. In this paper, we propose a computational model to calculate a user’s desirability as one of the most important factors which in determining the user’s emotions. The main purpose of this research is to find a relationship between personality and emotion in virtual learning environments. The model has been evaluated in a simulated virtual learning environment and the results show that the proposed model formulates the relationship between personality and emotions with high precision.


Personality Emotion User’s status Desirability 



This work is partially supported by the Iranian Cognitive Sciences and Technologies Council.


  1. Ames, C.: Motivation: what teachers need to know. Teach. Coll. 91(3), 409–421 (1990)Google Scholar
  2. Chaffar, S., Cepeda, G., Frasson, C.: Predicting the learner’s emotional reaction towards the tutor’s intervention. In: Proceedings of the 7th IEEE International Conference, Japan, pp. 639–641 (2007)Google Scholar
  3. Chaffar, S., Frasson, C.: Inducing optimal emotional state for learning in intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 45–54. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. Dang, T.H.H., Duhau, D.: GRACE – generic robotic architecture to create emotions. In: 11th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines - CLAWAR 2008, Coimbra, Portugal (2008)Google Scholar
  5. Damasio, A.R.: Descartes’ Error: Emotion, Reason, and the Human Brain. Gosset/Putnam Press, New York (1994)Google Scholar
  6. Durling, D., Cross, N., Johnson, J.: Personality and learning preferences of students in design and design-related disciplines. In: Proceedings of IDATER 1996 (International Conference on Design and Technology Educational Research), pp. 88–94. Loughborough University (1996)Google Scholar
  7. Egges, A., Kshirsagar, S., Magnenat-Thalmann, N.: Generic personality and emotion simulation for conversational agents. J. Comput. Anim. Virtual Worlds 15(1), 1–13 (2004)CrossRefGoogle Scholar
  8. Fatahi, S., Ghasem-Aghaee, N.: Design and implementation of an intelligent educational model based on personality and learner’s emotion. Int. J. Comput. Sci. Inf. Secur. vol. 7 (2010)Google Scholar
  9. Hall, E., Moseley, D.: Is there a role for learning styles in personalized [sic] education and training [Electronic version]? Int. J. Lifelong Educ. 24(3), 243–255 (2005)CrossRefGoogle Scholar
  10. Haron, N.B., Salim, N.B.: Empirical evaluation of mixed approach in adaptive hypermedia learning system. In: Proceedings of the Postgraduate Annual Research Seminar, pp. 244–249 (2006)Google Scholar
  11. Hartmann, P.: The five-factor model: psychometric, biological and practical perspectives. Nord. Psychol. 58(2), 150–170 (2006)CrossRefGoogle Scholar
  12. Higgs, M.: Is there a relationship between the Myers-Briggs type indicator and emotional intelligence? J. Manag. Psychol. 16(7), 509–533 (2001)CrossRefGoogle Scholar
  13. Hwang., J., Lee, K.C.: Exploring potentials of personality matching between users and target systems by using fuzzy cognitive map. In: 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 417–424 (2013)Google Scholar
  14. Jessee, S.A., ƠNeill, P.N., Dosch, R.O.: Matching student personality types and learning preferences to teaching methodologies. J. Dent. Educ. 70, 644–651 (2006)Google Scholar
  15. Kazemifard, M., Ghasem-Aghaee, N., Ören, T.I.: Design and implementation of GEmA: a generic emotional agent. Expert Syst. Appl. 38(3), 2640–2652 (2011)CrossRefGoogle Scholar
  16. Kim, J., Lee, A., Ryu, H.: Personality and its effects on learning performance: design guidelines for an adaptive e-learning system based on a user model. Int. J. Ind. Ergon. 1–12 (2013)Google Scholar
  17. Kort, B., Reilly, R.: Analytical models of emotions, learning and relationships: towards an affect-sensitive cognitive machine. MIT Media Lab Tech Report, vol. 43, no. 548 (2001)Google Scholar
  18. Mehdi, E.J., Nico, P., Julie, D., Bernard, P.: Modelling character emotion in an interactive virtual environment. In: Proceedings of AISB 2004 Symposium: Motion, Emotion and Cognition (2004). Google Scholar
  19. Moshkina, L.: An integrative framework for time-varying affective agent behavior. Institute of Technology, Georgia (2006)Google Scholar
  20. Niesler, A., Wydmuch, G.: User profiling in intelligent tutoring systems based on Myers-Briggs personality types. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol. I. International Association of Engineers, Hong Kong (2009)Google Scholar
  21. Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  22. Pittenger, D.J.: Measuring the MBTI… and coming up short. J. Career Plann. Employ. 54(1), 48–52 (1993)Google Scholar
  23. Reisz, Z., Boudreaux, M.J., Ozer, D.J.: Personality traits and the prediction of personal goals. Pers. Individ. Differ. 55(6), 699–704 (2013)CrossRefGoogle Scholar
  24. Rosis, D.F., Pelachaud, C., Poggi, I., Carolis, N., Carofiglio, V.: From Greta’s mind to her face: modeling the dynamics of affective states in a conversational agent, Embodied agent. Int. J. Hum. Comput Stud. 59(1), 81–118 (2003)CrossRefGoogle Scholar
  25. Salmela-Aro, K., et al.: Personal goals and personality traits among young adults: genetic and environmental effects. J. Res. Pers. 46(3), 248–257 (2012)CrossRefGoogle Scholar
  26. Santos, R., Marreiros, G., Ramos, C., Neves, J., Bulas-Cruz, J.: Personality, emotion, and mood in agent-based group decision making. J. Intell. Syst. 26(6), 58–66 (2011)Google Scholar
  27. Schultz, D.P., Schultz, S.E.: Theories of Personality. Wadsworth, Belmont (2008)zbMATHGoogle Scholar
  28. Trabelsi, A., Frasson, C.: The emotional machine: a machine learning approach to online prediction of user’s emotion and intensity. In: 10th International Conference on Advanced Learning Technologies (ICALT), pp. 613–617 (2010)Google Scholar
  29. Vincent, A., Ross, D.: Personalize training: determine learning styles, personality types and multiple intelligences online. Learn. Organ. 8(1), 36–43 (2001)CrossRefGoogle Scholar
  30. Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Tran. Pattern Anal. Mach. Intell. 31, 39–58 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Somayeh Fatahi
    • 1
    • 2
  • Hadi Moradi
    • 1
    • 3
    Email author
  • Ali Nouri Zonoz
    • 1
  1. 1.School of Electrical and Computer EngineeringUniversity of TehranTehranIran
  2. 2.Department of Computer ScienceDalhousie UniversityHalifaxCanada
  3. 3.Intelligent Systems Research InstituteSKKUSeoulSouth Korea

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