Abstract
This chapter describes a fully automated affect-sensitive Intelligent Tutoring System (ITS) called the Affective AutoTutor. AutoTutor is an ITS that helps students learn topics in Newtonian physics, computer literacy, and critical thinking via natural language dialogues that simulate the dialogue patterns observed in human–human tutoring. AutoTutor uses state-of-the-art natural language understanding mechanisms to model learners’ cognitive states and plan its dialogue moves in a manner that is sensitive to these states. While the original AutoTutor is sensitive to learners’ cognitive states, the affect-sensitive tutor is responsive to their affective states as well. This Affective tutor automatically detects learners’ boredom, confusion, and frustration by monitoring conversational cues, gross body language, and facial features. The sensed affective states guide the tutor’s responses in a manner that helps learners regulate their negative emotions. The tutor also synthesizes affect via the verbal content of its responses and the facial expressions and speech of an embodied pedagogical agent. An experiment comparing the affect-sensitive and nonaffective tutors indicated that the affective tutor improved learning for low-domain knowledge learners, particularly at deeper levels of comprehension.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Azevedo, R., & Chauncey, A. (2011). Integrating cognitive, metacognitive, and affective regulatory processes with MetaTutor. In R. Calvo & S. D’Mello (Eds.), Affective prospecting (Explorations in the learning sciences, instructional systems and performance). New York: Springer.
Baker, R., D’Mello, S., Rodrigo, M., & Graesser, A. (2010). Better to be frustrated than bored: The incidence and persistence of affect during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241.
Batson, C., Turk, C., Shaw, L., & Klein, T. (1995). Information function of empathic emotion – learning that we value the others welfare. Journal of Personality and Social Psychology, 68(2), 300–313.
Bruner, J. (1961). The act of discovery. Harvard Educational Review, 31(1), 21–32.
Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37.
Chi, M., Roy, M., & Hausmann, R. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301–341.
Clifford, M. (1988). Failure tolerance and academic risk-taking in ten- to twelve-year-old students. British Journal of Educational Psychology, 58, 15–27.
Clore, G. L., & Huntsinger, J. R. (2007). How emotions inform judgment and regulate thought. Trends in Cognitive Sciences, 11(9), 393–399.
Conati, C. (2011). Combining cognitive appraisal and sensors for affect detection in a framework for modeling user affect. In R. Calvo & S. D’Mello (Eds.), Affective prospecting (Explorations in the learning sciences, instructional systems and performance). New York: Springer.
Conati, C., Marsella, S., & Paiva, A. (2005). Affective interactions: The computer in the affective loop. In J. Riedl & A. Jameson (Eds.), Proceedings of the 10th International Conference on Intelligent User Interfaces (p. 7). New York: ACM.
Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning. Journal of Educational Media, 29, 241–250.
D’Mello, S., Craig, S., Sullins, J., & Graesser, A. (2006). Predicting affective states expressed through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue. International Journal of Artificial Intelligence in Education, 16(1), 3–28.
D’Mello, S., Craig, S., Witherspoon, A., McDaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18(1–2), 45–80.
D’Mello, S., & Graesser, A. (2009). Automatic detection of learners’ affect from gross body language. Applied Artificial Intelligence, 23(2), 123–150.
D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., et al. (2010). A time for emoting: When affect-sensitivity is and isn’t effective at promoting deep learning. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems (pp. 245–254). Berlin: Springer.
Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3–4), 169–200.
Ekman, P., & Friesen, W. (1978). The facial action coding system: A technique for the measurement of facial movement. Palo Alto: Consulting Psychologists Press.
Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
Fletcher, G. J. O., Danilovics, P., Fernandez, G., Peterson, D., & Reeder, G. D. (1986). Attributional complexity – An individual-differences measure. Journal of Personality and Social Psychology, 51(4), 875–884.
Graesser, A., Chipman, P., Haynes, B., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4), 612–618.
Graesser, A., Chipman, P., King, B., McDaniel, B., & D’Mello, S. (2007). Emotions and learning with AutoTutor. In R. Luckin, K. Koedinger, & J. Greer (Eds.), 13th International Conference on Artificial Intelligence in Education (pp. 569–571). Amsterdam: Ios Press.
Graesser, A., Jeon, M., & Dufty, D. (2008). Agent technologies designed to facilitate interactive knowledge construction. Discourse Processes, 45(4–5), 298–322.
Graesser, A., Lu, S. L., Jackson, G., Mitchell, H., Ventura, M., Olney, A., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180–193.
Graesser, A., McDaniel, B., Chipman, P., Witherspoon, A., D’Mello, S., & Gholson, B. (2006). Detection of emotions during learning with AutoTutor. Paper presented at the 28th Annual Conference of the Cognitive Science Society, Vancouver, Canada.
Graesser, A., & Olde, B. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95(3), 524–536.
Graesser, A., Penumatsa, P., Ventura, M., Cai, Z., & Hu, X. (2007). Using LSA in AutoTutor: Learning through mixed-initiative dialogue in natural language. In T. Landauer, D. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis (pp. 243–262). Mahwah, NJ: Erlbaum.
Graesser, A., Person, N., & Magliano, J. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology, 9(6), 495–522.
Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.
Hidi, S. (2006). Interest: A unique motivational variable. Educational Research Review, 1, 69–82.
Isen, A. (2008). Some ways in which positive affect influences decision making and problem solving. In M. Lewis, J. Haviland-Jones, & L. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 548–573). New York, NY: Guilford.
Johnson, W., Rickel, J., & Lester, J. (2000). Animated Pedagogical Agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 11, 47–78.
Johnstone, T., & Scherer, K. (2000). Vocal communication of emotion. In M. Lewis & J. Haviland-Jones (Eds.), Handbook of emotions (2nd ed., pp. 220–235). New York: Guilford Press.
Jonassen, D., Peck, K., & Wilson, B. (1999). Learning with technology: A constructivist perspective. Upper Saddle River, NJ: Prentice Hall.
Lehman, B., D’Mello, S., & Person, N. (2008). All alone with your emotions: An analysis of student emotions during effortful problem solving activities. Paper presented at the Workshop on Emotional and Cognitive issues in ITS at the Ninth International Conference on Intelligent Tutoring Systems
Lehman, B., Matthews, M., D’Mello, S., & Person, N. (2008). What are you feeling? Investigating student affective states during expert human tutoring sessions. In B. Woolf, E. Aimeur, R. Nkambou, & S. Lajoie (Eds.), Proceedings of the 9th International Conference on Intelligent Tutoring Systems (pp. 50–59). Berlin: Springer.
McAuley, E., Duncan, T. E., & Russell, D. W. (1992). Measuring causal attributions – the Revised Causal Dimension Scale (Cdsii). Personality and Social Psychology Bulletin, 18(5), 566–573.
McNeese, M. (2003). New visions of human-computer interaction: making affect compute. International Journal of Human-Computer Studies, 59(1–2), 33–53.
Meyer, D., & Turner, J. (2006). Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review, 18(4), 377–390.
Moreno, R., Mayer, R., Spires, H., & Lester, J. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19(2), 177–213.
Moshman, D. (1982). Exogenous, endogenous, and dialectical constructivism. Developmental Review, 2(4), 371–384.
Paiva, A., Prada, R., & Picard, R. (Eds.). (2007). Affective computing and intelligent interaction. Heidelberg: Springer.
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. Calvo & S. D’Mello (Eds.), Affective prospecting (Explorations in the learning sciences, instructional systems and performance). New York: Springer.
Piaget, J. (1952). The origins of intelligence. New York: International University Press.
Picard, R. (1997). Affective computing. Cambridge: MIT Press.
Pour, P. A., Hussein, S., AlZoubi, O., D’Mello, S. K., & Calvo, R. (2010). The impact of system feedback on learners’ affective and physiological states. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems (pp. 264–273). Berlin, Heidelberg: Springer.
Rumelhart, D., McClelland, J., & PDP Research Group. (1986). Parallel distributed processing: explorations in the microstructure of cognition. Volume 1: Foundations. Cambridge, MA: MIT Press.
Rus, V., & Graesser, A. (2007). Lexico-syntactic subsumption for textual entailment. In N. Nicolov, K. Bontcheva, G. Angelova, & R. Mitkov (Eds.), Recent advances in natural language processing IV: Selected papers from RANLP 2005 (pp. 187–196). Amsterdam: John Benjamins.
Storey, J., Kopp, K., Wiemer, K., Chipman, P., & Graesser, A. (in press). Critical thinking tutor: Using AutoTutor to teach scientific critical thinking skills. Behavioral Research Methods.
VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education., 16(3), 227–265.
VanLehn, K., Graesser, A., Jackson, G., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31(1), 3–62.
Vavik, L. (1993). Facilitating discovery learning in computer-based simulation learning environments. In R. Tennyson & A. Baron (Eds.), Automating instructional design: Computer-based development and delivery tools (pp. 403–449). Berlin: Springer.
Weiner, B. (1986). An attributional theory of motivation and emotion. New York: Springer.
Acknowledgments
We thank our research colleagues in the Emotive Computing Group and the Tutoring Research Group (TRG) at the University of Memphis (http://emotion.autotutor.org). We gratefully acknowledge our partners in the Affective Computing group at the MIT Media Lab.
This research was supported by the National Science Foundation (REC 0106965, ITR 0325428, HCC 0834847). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
D’Mello, S.K., Lehman, B., Graesser, A. (2011). A Motivationally Supportive Affect-Sensitive AutoTutor. 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_9
Download citation
DOI: https://doi.org/10.1007/978-1-4419-9625-1_9
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9624-4
Online ISBN: 978-1-4419-9625-1
eBook Packages: Humanities, Social Sciences and LawEducation (R0)