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Temporal Generalizability of Face-Based Affect Detection in Noisy Classroom Environments

  • Nigel BoschEmail author
  • Sidney D’Mello
  • Ryan Baker
  • Jaclyn Ocumpaugh
  • Valerie Shute
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9112)

Abstract

The goal of this paper was to explore the possibility of generalizing face-based affect detectors across multiple days, a problem which plagues physiological-based affect detection. Videos of students playing an educational physics game were collected in a noisy computer-enabled classroom environment where students conversed with each other, moved around, and gestured. Trained observers provided real-time annotations of learning-centered affective states (e.g., boredom, confusion) as well as off-task behavior. Detectors were trained using data from one day and tested on data from different students on another day. These cross-day detectors demonstrated above chance classification accuracy with average Area Under the ROC Curve (AUC, .500 is chance level) of .658, which was similar to within-day (training and testing on data collected on the same day) AUC of .667. This work demonstrates the feasibility of generalizing face-based affect detectors across time in an ecologically valid computer-enabled classroom environment.

Keywords

Affective State Temporal Generalizability Intelligent Tutoring System Feature Ranking Educational Data Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    D’Mello, S.: A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology. 105, 1082–1099 (2013)CrossRefGoogle Scholar
  2. 2.
    Schutz, P., Pekrun, R. (eds.): Emotion in Education. Academic Press, San Diego, CA (2007)Google Scholar
  3. 3.
    Bosch, N., D’Mello, S., Mills, C.: What emotions do novices experience during their first computer programming learning session? In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 11–20. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Pardos, Z.A., Baker, R.S.J.D., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M.: Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 117–124. ACM, New York (2013)Google Scholar
  5. 5.
    Fiedler, K., Beier, S.: Affect and cognitive processes in educational contexts. International handbook of emotions in education, pp. 36–56 (2014)Google Scholar
  6. 6.
    D’Mello, S., Blanchard, N., Baker, R., Ocumpaugh, J., Brawner, K.: I feel your pain: a selective review of affect-sensitive instructional strategies. In: Sottilare, R., Graesser, A., Hu, X., and Goldberg, B. (eds.) Design Recommendations for Intelligent Tutoring Systems – vol. 2: Instructional Management, pp. 35–48 (2014)Google Scholar
  7. 7.
    D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K., Perkins, L., Graesser, A.: A time for emoting: when affect-sensitivity is and isn’t effective at promoting deep learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) Intelligent Tutoring Systems. Lecture Notes in Computer Science, vol. 6094, pp. 245–254. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. AIED, pp. 17–24 (2009)Google Scholar
  9. 9.
    Alzoubi, O., Hussain, M., D’Mello, S., Calvo, R.A.: Affective Modeling from Multichannel Physiology: Analysis of Day Differences. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 4–13. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    D’Mello, S., Kory, J.: Consistent but modest: a meta-analysis on unimodal and multimodal affect detection accuracies from 30 studies. In: Proceedings of the 14th ACM international conference on Multimodal interaction, pp. 31–38. ACM, New York (2012)Google Scholar
  11. 11.
    Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence. 23, 1175–1191 (2001)CrossRefGoogle Scholar
  12. 12.
    Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31, 39–58 (2009)CrossRefGoogle Scholar
  13. 13.
    Grafsgaard, J.F., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Automatically recognizing facial expression: predicting engagement and frustration. In: Proceedings of the 6th International Conference on Educational Data Mining (2013)Google Scholar
  14. 14.
    Kapoor, A., Burleson, W., Picard, R.W.: Automatic prediction of frustration. International Journal of Human-Computer Studies. 65, 724–736 (2007)CrossRefGoogle Scholar
  15. 15.
    Whitehill, J., Serpell, Z., Lin, Y.-C., Foster, A., Movellan, J.R.: The faces of engagement: Automatic recognition of student engagement from facial expressions. IEEE Transactions on Affective Computing. 5, 86–98 (2014)CrossRefGoogle Scholar
  16. 16.
    Bosch, N., Chen, Y., D’Mello, S.: It’s written on your face: detecting affective states from facial expressions while learning computer programming. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 39–44. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  17. 17.
    Bosch, N., D’Mello, S., Baker, R., Ocumpaugh, J., Shute, V.J., Ventura, M., Wang, L., Zhao, W.: Automatic detection of learning-centered affective states in the wild. In: Proceedings of the 2015 International Conference on Intelligent User Interfaces (IUI 2015). ACM, New York, NY, USA (in Press)Google Scholar
  18. 18.
    Shute, V.J., Ventura, M., Kim, Y.J.: Assessment and learning of qualitative physics in Newton’s Playground. The Journal of Educational Research. 106, 423–430 (2013)CrossRefGoogle Scholar
  19. 19.
    Ocumpaugh, J., Baker, R., Rodrigo, M.M.T.: Baker-Rodrigo observation method protocol (BROMP) 1.0. Training manual version 1.0. Technical Report. New York, NY: EdLab. Manila, Philippines: Ateneo Laboratory for the Learning Sciences (2012)Google Scholar
  20. 20.
    Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (CERT). In: 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011), pp. 298–305 (2011)Google Scholar
  21. 21.
    Ekman, P., Friesen, W.V.: Facial action coding system. Consulting Psychologist Press, Palo Alto, CA (1978)Google Scholar
  22. 22.
    Kory, J., D’Mello, S., Olney, A.: Motion Tracker: Cost-effective, non-intrusive, fully-automated monitoring of bodily movements using motion silhouettes. Presented at the (in review)Google Scholar
  23. 23.
    Allison, P.D.: Multiple regression: a primer. Pine Forge Press (1999)Google Scholar
  24. 24.
    Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., Raedt, L.D. (eds.) Machine Learning: ECML-94. Lecture Notes in Computer Science, vol. 784, pp. 171–182. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  25. 25.
    Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench. In: Proceedings of the Second Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361 (1994)Google Scholar
  26. 26.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research. 16, 321–357 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nigel Bosch
    • 1
    Email author
  • Sidney D’Mello
    • 1
    • 2
  • Ryan Baker
    • 3
  • Jaclyn Ocumpaugh
    • 3
  • Valerie Shute
    • 4
  1. 1.Departments of Computer ScienceUniversity of Notre DameNotre DameUSA
  2. 2.PsychologyUniversity of Notre DameNotre DameUSA
  3. 3.Department of Human Development, Teachers CollegeColumbia UniversityNew YorkUSA
  4. 4.Department of Educational Psychology and Learning SystemsFlorida State UniversityTallahasseeUSA

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