Abstract
This chapter delineates some of the pedagogy needed by a motivationally intelligent tutoring system. The learner in a negative motivational state (e.g. demotivated, unmotivated or poorly motivated) may present with behavioural symptoms such as lack of effort, avoiding risk, “gaming the system” and so on. Such states are accompanied by a variety of negative feelings such as anxiety, boredom, or frustration. Making an effective pedagogic judgement, first, as to the nature of the symptoms and feelings, second, as to their causes and third, as to how best the motivational state might be remediated is no easy task, either for a human teacher or for a system. The paper adopts a view of motivation, developed by Pintrich (Psychol Educ Psychol 7: 2003), in terms of three broad facets: one concerned with Values, one concerned with Expectancies and one concerned with Feelings. Three negative learner motivational states are described in terms of their main associated feeling. For each state the paper suggests pedagogical tactics that might be deployed depending on whether the underlying cause of the state is rooted in issues around Values or in issues around Expectancies. So, for example, a distinction is drawn between frustration caused by the learner’s belief in her inability to solve the problem in hand (Expectancies) from frustration caused by her desire to be doing something else altogether (Values).
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References
Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. In V. Dimitrova, R. Mizoguchi, B. du Boulay & A. Grasser (Eds.), Artificial intelligence in education. Building learning systems that care: From knowledge representation to affective modelling (Vol. Frontiers in Artificial Intelligence and Applications 200, pp. 17–24). Amsterdam: IOS Press.
Avramides, K., & du Boulay, B. (2009). Motivational Diagnosis in ITSs: Collaborative, reflective self-report. In V. Dimitrova, R. Nizoguchi, B. du Boulay & A. Graesser (Eds.), Artificial intelligence in education. Building learning systems that care: From knowledge representation to affective modelling (Vol. Frontiers in Artificial Intelligence and Applications 200, pp. 587–589). Amsterdam: IOS Press.
Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in “gaming the system” behaviours in interactive learning environments. Journal of Interactive Learning Research, 19(2), 185–224.
Balaam, M., Fitzpatrick, G., Good, J., & Luckin, R. (2010). Exploring affective technologies for the classroom with the subtle stone. Paper presented at the Proceedings of the 28th international conference on Human factors in computing systems (CHI 2010). Atlanta, Georgia.
Balaam, M., Luckin, R., & Good, J. (2009). Supporting affective communication in the classroom with the Subtle Stone. International Journal of Learning Technology, 4(3–4), 188–215.
Bickhard, M. H. (2003). An integration of motivation and cognition BJEP Monograph Series II, Number 2 – Development and Motivation (Vol. 1, pp. 41–56). British Psychological Society. Leicester, UK.
Blanchard, E. G., Volfson, B., Hong, Y. -J., & Lajoie, S. P. (2009). Affective artificial intelliegnce in education: From detection to adaptation. In V. Dimitrova, R. Mizoguchi, B. du Boulay & A. Grasser (Eds.), Artificial intelligence in education. Building learning systes that care: From knowledge representation to affective modelling (Vol. Frontiers in Artificial Intelligence and Applications 200, pp. 81–88). Amsterdam: IOS Press.
Conati, C., & Maclaren, H. (2005). Data-driven refinement of a probabilistic model of user affect. In L. Ardissono, P. Brna & A. Mitrovic (Eds.), User Modeling 2005, 10th International Conference, UM 2005, Edinburgh, Scotland, UK, Proceedings (Vol. Lecture Notes in Artificial Intelligence 3538, pp. 40–49). Berlin: Springer.
D’Mello, S., & Graesser, A. (2010). Mining Bodily Patterns of affective experience during learning. In R. S. J. d. Baker, A. Merceron & P. I. J. Pavlik (Eds.), Proceedings of the 3rd International Conference on Educational Data Mining (pp. 31–40). Pittsburgh.
D’Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., et al. (2008). AutoTutor Detects and Responds to Learners Affective and Cognitive States. Paper presented at the Workshop on Emotional and Cognitive Issues at the International Conference on Intelligent Tutoring Systems. Montreal, Canada.
del Soldato, T., & du Boulay, B. (1995). Implementation of motivational tactics in tutoring systems. International Journal of Artificial Intelligence in Education, 6(4), 337–378.
Dong, A. (2011). The role of affect in creative minds. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.
du Boulay, B., Rebolledo-Mendez, G. R., Luckin, R., & Martinez-Miron, E. (2007). Motivationally intelligent systems: Diagnosis and feedback. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. Frontiers in Artificial Intelligence and Applications 158, pp. 563–565). Amsterdam: IOS Press.
Graesser, A. C., D’Mello, S. K., Craig, S. D., Witherspoon, A., Sullins, J., McDaniel, B., et al. (2008). The relationship between affective states and dialog patterns during interactions with auto tutor. Journal of Interactive Learning Research, 19(2), 293–312.
Johns, J., & Woolf, B. P. (2006). A dynamic mixture model to detect student motivation and proficiency. Paper presented at the Proceedings of the 21st national Conference on Articifial Intelligence (AAAI-06). Boston, MA.
Kapoor, A., Burleson, W., & Picard, R. W. (2007). Automatic prediction of frustration. International Journal of Human-Computer Studies, 65(8), 724–736.
Ortony, A., Clore, G. L., & Collins, A. (1988). The cognitive structure of emotions. Cambridge: Cambridge University Press.
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. Calvo & S. D’Mello (Eds.), Explorations in the learning sciences, instructional systems and performance technologies. New York: Springer.
Pintrich, P. (2003). Motivation and classroom learning. Handbook of Psychology: Educational Psychology, 7, 103–122.
Rosiek, J. (2003). Emotional scaffolding: An exploration of the teacher knowledge at the intersection of student emotion and the subject matter. Journal of Teacher Education, 54(4), 399–412.
Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.
Weber, G., & Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for web-based instruction. International Journal of Artificial Intelligence in Education, 12(4), 351–384.
Zakharov, K., Mitrovic, A., & Johnston, L. (2008). Towards emotionally-intelligent pedagogical agents. In B. P. Woolf, E. Aïmeur, R. Nkambou & S. L. Lajoie (Eds.), Intelligent Tutoring Systems, 9th International Conference, ITS 2008, Montreal, Canada, Proceedings (Vol. Lecture Notes in Computer Science 5091, pp. 19–28). Berlin: Springer.
Acknowledgments
I thank Katerina Avramides, Madeline Balaam, Martin van Zijl and the Intelligent Computer Tutor Group at University of Canterbury, New Zealand for many helpful comments on a draft of this paper.
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Boulay, B.d. (2011). Towards a Motivationally Intelligent Pedagogy: How Should an Intelligent Tutor Respond to the Unmotivated or the Demotivated?. In: Calvo, R., D'Mello, S. (eds) New Perspectives on Affect and Learning Technologies. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9625-1_4
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