Dynamic Approaches to Modeling Student Affect and its Changing Role in Learning and Performance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)


We investigate the relation between students’ affect, learning and performance in the context of the ASSISTments online math tutoring system. Moment-by-moment estimates of students’ affective states derived from a series of affect detectors accompany each student response within the tutoring system. By applying a series modified factorial hidden Markov models that account for students’ affective state at the time of the given response and comparing the models’ performance to the standard Bayesian Knowledge Tracing (BKT) approach, we evaluate the impact of affect on estimates of students’ guess and slip behavior. The investigation suggests a model based approach to improving student models in the context of online tutoring systems.


Knowledge tracing Emotion Affect Learning Performance Hidden markov models Factorial hidden markov models Automated tutoring systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Atkinson, R.C.: Ingredients for a theory of instruction. American Psychologist 27(10), 921 (1972)CrossRefGoogle Scholar
  2. 2.
    Baker, R.S., Corbett, A.T., Koedinger, K.R.: Detecting student misuse of intelligent tutoring systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 531–540. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    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. International Journal of Human-Computer Studies 68(4), 223–241 (2010)CrossRefGoogle Scholar
  4. 4.
    Barrett, L.F.: Variety is the spice of life: A psychological construction approach to understanding variability in emotion. Cognition and Emotion 23(7), 1284–1306 (2009)CrossRefGoogle Scholar
  5. 5.
    Buff, A., Reusser, K., Rakoczy, K., Pauli, C.: Activating positive affective experiences in the classroom: “Nice to have” or something more? Learning and Instruction 21(3), 452–466 (2011)CrossRefGoogle Scholar
  6. 6.
    Corbett, A.T., Anderson, J.R., O’Brien, A.T.: Student modeling in the ACT Programming Tutor. Cognitively diagnostic assessment, 19–41 (1995)Google Scholar
  7. 7.
    Csikszentmihalyi, M.: Flow: The psychology of optimal performance. Cambridge University Press, NY (1990)Google Scholar
  8. 8.
    Wilson, M., De Boeck, P.: Descriptive and explanatory item response models, pp. 43–74. Springer, New York (2004)CrossRefGoogle Scholar
  9. 9.
    D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learning and Instruction 22(2), 145–157 (2012)CrossRefGoogle Scholar
  10. 10.
    D’Mello, S., Craig, S., Gholson, B., Franklin, S., Picard, R., Graesser, A.: Integrating Affect Sensors in an Intelligent Tutoring System. In: Proc. Computer in the Affective Loop Workshop at 2005 Int’l Conf. Intelligent User Interfaces, pp. 7–13 (2005)Google Scholar
  11. 11.
    Graesser, A.C., D’Mello, S.K., Craig, S.D., Witherspoon, A., Sullins, J., McDaniel, B., Gholson, B.: The Relationship between Affective States and Dialog Patterns during Interactions with AutoTutor. Journal of Interactive Learning Research 19(2), 293–312 (2008)Google Scholar
  12. 12.
    Dweck, C.S.: Messages that motivate: How praise molds students’ beliefs, motivation, and performance (in surprising ways) (2002)Google Scholar
  13. 13.
    Frijda, N.H.: Emotion experience and its varieties. Emotion Review 1(3), 264–271 (2009)CrossRefGoogle Scholar
  14. 14.
    Immordino-Yang, M.H., Damasio, A.: We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, brain, and education 1(1), 3–10 (2007)CrossRefGoogle Scholar
  15. 15.
    Huk, T., Ludwigs, S.: Combining cognitive and affective support in order to promote learning. Learning and Instruction 19(6), 495–505 (2009)CrossRefGoogle Scholar
  16. 16.
    Johns, J., Woolf, B. (July 2006): A dynamic mixture model to detect student motivation and proficiency. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, no. 1, p. 163. AAAI Press, Menlo Park. MIT Press, Cambridge (1999)Google Scholar
  17. 17.
    Linnenbrink-Garcia, L., Pekrun, R.: Students’ emotions and academic engagement: Introduction to the special issue. Contemporary Educational Psychology 36(1), 1–3 (2011)CrossRefGoogle Scholar
  18. 18.
    Meyer, D.K., Turner, J.C.: Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review 18(4), 377–390 (2006)CrossRefGoogle Scholar
  19. 19.
    Murphy, K.: The Bayes Net Toolbox for Matlab. Computing Science and Statistics 33(2), 1024–1034 (2001). ChicagoGoogle Scholar
  20. 20.
    Pardos, Z.A., Heffernan, N.T.: Modeling individualization in a bayesian networks implementation of knowledge tracing. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 255–266. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Pekrun, R., Stephens, E.J.: Achievement Emotions: A Control-Value Approach. Social and Personality Psychology Compass 4(4), 238–255 (2010)CrossRefGoogle Scholar
  22. 22.
    Pekrun, R., Linnenbrink-Garcia, L.: Academic emotions and student engagement. In: Handbook of research on student engagement, pp. 259–282. Springer US (2012)Google Scholar
  23. 23.
    Reye, J.: Student modelling based on belief networks. International Journal of Artificial Intelligence in Education: 14(63), 96 (2004)Google Scholar
  24. 24.
    Russell, J.A.: Core affect and the psychological construction of emotion. Psychological review 110(1), 145 (2003)CrossRefGoogle Scholar
  25. 25.
    San Pedro, M.O.Z., Baker, R.S., Gowda, S.M., Heffernan, N.T.: Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 41–50. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  26. 26.
    Scherer, K.R.: The dynamic architecture of emotion: Evidence for the component process model. Cognition and emotion 23(7), 1307–1351 (2009)CrossRefGoogle Scholar
  27. 27.
    Schutz, P.A., Pekrun, R.: Introduction to emotion in education. Emotion in education, 3–10 (2007)Google Scholar
  28. 28.
    Verhelst, N.D., Glas, C.A.: A dynamic generalization of the Rasch model. Psychometrika 58(3), 395–415 (1993)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of EducationUniversity of CaliforniaBerkeleyUSA
  2. 2.School of InformationUniversity of CaliforniaBerkeleyUSA

Personalised recommendations