Skip to main content
SpringerLink
Log in

Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System

  • Conference paper
Intelligent Tutoring Systems (ITS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8474))

Included in the following conference series:

Abstract

In this paper we investigate the usefulness of eye tracking data for predicting emotions relevant to learning, specifically boredom and curiosity. The data was collected during a study with MetaTutor, an intelligent tutoring system (ITS) designed to promote the use of self-regulated learning strategies. We used a variety of machine learning and feature selection techniques to predict students’ self-reported emotions from gaze data features. We examined the optimal amount of interaction time needed to make predictions, as well as which features are most predictive of each emotion. The findings provide insight into how to detect when students disengage from MetaTutor.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. AlZoubi, O., D’Mello, S., Calvo, R.: Detecting naturalistic expressions of nonbasic affect using physiological signals (2012)

    Google Scholar 

  2. Anderson, J.R., Gluck, K.: What role do cognitive architectures play in intelligent tutoring systems. Cognition & Instruction, 227–262 (2001)

    Google Scholar 

  3. Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: AIED, July 6-10, pp. 17–24. IOS Press, Brighton (2009)

    Google Scholar 

  4. Azevedo, R., Harley, J., Trevors, G., et al.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: IHMLT, pp. 427–449. Springer (2013)

    Google Scholar 

  5. Azevedo, R., Johnson, A., Chauncey, A., Burkett, C.: Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In: New Science of Learning, pp. 225–247. Springer (2010)

    Google Scholar 

  6. Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J.M., Azevedo, R., Bouchet, F.: Inferring Learning from Gaze Data during Interaction with an Environment to Support Self-Regulated Learning. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 229–238. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  7. Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. UMUAI 19(3), 267–303 (2009)

    Google Scholar 

  8. D’Mello, S., Graesser, A.: Mind and body: Dialogue and posture for affect detection in learning environments. FAIA 158, 161 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  10. D’Mello, S., Olney, A., Williams, C., Hays, P.: Gaze tutor: A gaze-reactive intelligent tutoring system. IJHCS 70(5), 377–398 (2012)

    Google Scholar 

  11. D’mello, S., Craig, S., Gholson, B., Franklin, S., Picard, R., Graesser, A.: Integrating affect sensors in an intelligent tutoring system. Affective Interactions, 7–13 (2005)

    Google Scholar 

  12. Field, A.: Discovering statistics using SPSS. Sage publications (2009)

    Google Scholar 

  13. 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: NAACL: Human Language Technologies, pp. 91–102 (2012)

    Google Scholar 

  14. Gutica, Conati: Student Emotions with an Edu-Game: A Detailed Analysis (2013)

    Google Scholar 

  15. Harley, J.M., Bouchet, F., Azevedo, R.: Aligning and Comparing Data on Emotions Experienced during Learning with MetaTutor. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 61–70. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Hussain, M.S., Calvo, R.A.: Multimodal affect detection from physiological and facial features during ITS interaction. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 472–474. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Kardan, S., Conati, C.: Exploring gaze data for determining user learning with an interactive simulation. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 126–138. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  18. Klein, J., Moon, Y., Picard, R.W.: This computer responds to user frustration: Theory, design, and results. Interacting with Computers 14(2), 119–140 (2002)

    Article  Google Scholar 

  19. Kort, B., Reilly, R., Mostow, J., Picard, R.: Experimentally augmenting an intelligent tutoring system with human-supplied capabilities: Adding human-provided emotional scaf-folding to an automated reading tutor that listens. In: ICMI, p. 483 (2002)

    Google Scholar 

  20. Muldner, K., Christopherson, R., Atkinson, R., Burleson, W.: Investigating the utility of eye-tracking information on affect and reasoning for user modeling. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 138–149. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  21. Pekrun, R., Goetz, T., Titz, W., Perry, R.P.: Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist 37(2), 91–105 (2002)

    Article  Google Scholar 

  22. Pekrun, R.: Emotions as drivers of learning and cognitive development. In: New Perspectives on Affect and Learning Technologies, pp. 23–39. Springer (2011)

    Google Scholar 

  23. Prendinger, H., Ishizuka, M.: The empathic companion: A character-based interface that addresses users’affective states. APAI 19(3-4), 267–285 (2005)

    Google Scholar 

  24. Sabourin, J., Mott, B., Lester, J.C.: Modeling learner affect with theoretically grounded dynamic bayesian networks. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 286–295. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  25. Sabourin, J., Shores, L., Mott, B., Lester, J.: Predicting student self-regulation strategies in game-based learning environments, 141–150 (2012)

    Google Scholar 

  26. Sibert, J.L., Gokturk, M., Lavine, R.A.: The reading assistant: Eye gaze triggered auditory prompting for reading remediation, pp. 101–107. ACM Press (2000)

    Google Scholar 

  27. Silvia, P.J.: Interest—The curious emotion. Current Directions in Psychological Science 17(1), 57–60 (2008)

    Article  Google Scholar 

  28. Smilek, D., Carriere, J.S., Cheyne, J.A.: Out of Mind, Out of Sight Eye Blinking as Indicator and Embodiment of Mind Wandering. Psych. Sci. 21(6), 786–789 (2010)

    Article  Google Scholar 

  29. Wang, H., Chignell, M., Ishizuka, M.: Empathic tutoring software agents using real-time eye tracking. ETRA, 73–78 (2006)

    Google Scholar 

  30. Wang, N., Johnson, W.L., Mayer, R.E., Rizzo, P., Shaw, E., Collins, H.: The politeness effect: Pedagogical agents and learning outcomes. IJHCS 66(2), 98–112 (2008)

    Google Scholar 

  31. Wang, W., Li, Z., Wang, Y., Chen, F.: Indexing cognitive workload based on pupillary response under luminance and emotional changes. IUI, 247–256 (2013)

    Google Scholar 

  32. Zimmerman, B.J.: Self-efficacy: An essential motive to learn. Contemporary Educational Psychology 25(1), 82–91 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of British Columbia, 2366 Main Mall, Vancouver, BC, Canada, V6T1Z4

    Natasha Jaques & Cristina Conati

  2. McGill University, 845 Sherbrook Street West, Montreal, QC, Canada, H3A0G4

    Jason M. Harley

  3. North Carolina State University, 2310 Stinson Drive, Raleigh, NC, USA, 27695

    Roger Azevedo

Authors
  1. Natasha Jaques
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristina Conati
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jason M. Harley
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Roger Azevedo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Department, University Politehnica of Bucharest, Splaiul Independentei 313, 060042, Bucharest, Romania

    Stefan Trausan-Matu

  2. North Carolina State University, 890 Oval Dr., Campus Box 8206, 27695-8206, Raleigh, NC, USA

    Kristy Elizabeth Boyer

  3. Department of Information and Computer Sciences, University of Hawaii, 1680 East-West Road, POST 317, 96822, Honolulu, HI, USA

    Martha Crosby

  4. Neoanalysis Ltd., Marni 56, 10437, Athens, Greece

    Kitty Panourgia

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Jaques, N., Conati, C., Harley, J.M., Azevedo, R. (2014). Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07221-0_4

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07220-3

  • Online ISBN: 978-3-319-07221-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation