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Sensor-Free Affect Detection for a Simulation-Based Science Inquiry Learning Environment

  • Luc Paquette
  • Ryan S. J. D. Baker
  • Michael A. Sao Pedro
  • Janice D. Gobert
  • Lisa Rossi
  • Adam Nakama
  • Zakkai Kauffman-Rogoff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

Recently, there has been considerable interest in understanding the relationship between student affect and cognition. This research is facilitated by the advent of automated sensor-free detectors that have been designed to “infer” affect from the logs of student interactions within a learning environment. Such detectors allow for fine-grained analysis of the impact of different affective states on a range of learning outcome measures. However, these detectors have to date only been developed for a subset of online learning environments, including problem-solving tutors, dialogue tutors, and narrative-based virtual environments. In this paper, we extend sensor-free affect detection to a science microworld environment, affording the possibility of more deeply studying and responding to student affect in this type of learning environment.

Keywords

Educational data mining affect detection affective computing 

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References

  1. 1.
    Baker, R.S.J.D., 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
  2. 2.
    Baker, R.S.J.d., Moore, G.R., Wagner, A.Z., Kalka, J., Salvi, A., Karabinos, M., Ashe, C.A., Yaron, D.: The Dynamics Between Student Affect and Behavior Occurring Outside of Educational Software. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 14–24. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    D’Mello, S.K., Taylor, R., Grasser, A.C.: Monitoring Affective Trajectories During Complex Learning. In: Proceedings of the 29th Annual Cognitive Science Society, pp. 203–208 (2007)Google Scholar
  4. 4.
    Dragon, T., Arroyo, I., Woolf, B.P., Burleson, W., el Kaliouby, R., Eydgahi, H.: Viewing Student Affect and Learning Through Classroom Observation and Physical Sensors. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 29–39. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Lee, D.M.C., Rodrigo, M. M.T., Baker, R.S.J.d., Sugay, J.O., Coronel, A.: Exploring the Relationship Between Novice Programmer Confusion and Achievement. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 175–184. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Sabourin, J., Rowe, J.P., Mott, B.W., Lester, J.C.: When Off-Task in On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 534–536. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Conati, C., Maclaren, H.: Empirically Building and Evaluating a Probabilistic Model of User Affect. UMUAI 19, 267–303 (2009)Google Scholar
  8. 8.
    Baker, R.S.J.d., et al.: Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra. In: Proceedings of EDM 2012, pp. 126–133 (2012)Google Scholar
  9. 9.
    Pardos, Z., Baker, R.S.J.d., San Pedro, M.O.Z., Gowda, S.M., Gowda, S.: Affective States and State Tests: Investigating how Affect Throughout the School Year Predicts End of Year Learning Outcomes. In: Proceedings of LAK 2013, pp. 117–124 (2013)Google Scholar
  10. 10.
    D’Mello, S.K., Craig, S.D., Witherspoon, A.W., McDaniel, B.T., Graesser, A.C.: Automatic Detection of Learner’s Affect from Conversational Cues. UMUAI 18, 45–80 (2008)Google Scholar
  11. 11.
    Litman, D.J., Forbes-Riley, K.: Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogue with Both Humans and Computer-Tutors. Speech Communication 48(5), 559–590 (2006)CrossRefGoogle Scholar
  12. 12.
    Sabourin, J., Mott, B., Lester, J.: 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)CrossRefGoogle Scholar
  13. 13.
    Gobert, J., Sao Pedro, M., Baker, R., Toto, E., Montalvo, O.: Leveraging Educational Data Mining for Real Time Performance Assessment of Scientific Inquiry Skills within Microworlds. JEDM 4(1), 111–143 (2012)Google Scholar
  14. 14.
    Metcalf, S.J., Kamarainen, A., Grotzer, T.A., Dede, C.J.: Ecosystem Science Learning via Multi-User Virtual Environments. In: AERA Conference (2011)Google Scholar
  15. 15.
    Hershkovitz, A., Baker, R.S.J.d., Gobert, J., Nakama, A.: A Data-Driven Path Model of Student Attributes, Affect, and Engagement in a Computer-Based Science Inquiry Microworld. In: Proceedings of the ICLS (2012)Google Scholar
  16. 16.
    NGSS Lead States: Next Generation Science Standards: For States, By States.The National Academies Press, Washington (2013)Google Scholar
  17. 17.
    Sao Pedro, M., Baker, R., Gobert, J., Montalvo, O., Nakama, A.: Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. UMUAI 23, 1–39 (2013)Google Scholar
  18. 18.
    Bartel, C.A., Saavedra, R.: The Collective Construction of Work Group Moods. Administrative Science Quarterly 45, 197–231 (2001)CrossRefGoogle Scholar
  19. 19.
    Planalp, S., DeFrancisco, V.L., Rutherford, D.: Varieties of Cues to Emotion in Naturally Occurring Situations. Cognition and Emotion 10(2), 137–153 (1996)CrossRefGoogle Scholar
  20. 20.
    Ocumpaugh, J., Baker, R.S.J.d., 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
  21. 21.
    Litman, D.J., Forbes-Riley, L.: Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with Both Human and Computer Tutors. Speech Communication 48(5), 559–590 (2006)CrossRefGoogle Scholar
  22. 22.
    Rodrigo, M.M.T., et al.: Comparing Learners’ Affect While Using an Intelligent Tutoring Systems and a Simulation Problem Solving Game. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 40–49. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  23. 23.
    Baker, R.S.J.d., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-Task Behavior in the Cognitive Tutor Classroom: When Students “Game the System”. In: Proceedings of ACM CHI 2004: Computer-Human Interaction, pp. 383–390 (2004)Google Scholar
  24. 24.
    Sao Pedro, M., Baker, R., Gobert, J., Montalvo, O., Nakama, A.: Levaraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. User Modeling and User-Adapted Interaction 23, 1–39 (2013)CrossRefGoogle Scholar
  25. 25.
    Cohen, J.: A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20(1), 37–46 (1960)CrossRefGoogle Scholar
  26. 26.
    Hanley, J., McNeil, B.: The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve. Radiology 143, 29–36 (1982)Google Scholar
  27. 27.
    Woolf, B.P., Arroyo, I., Cooper, D., Burleson, W., Muldner, K.: Affective Tutors: Automatic Detection of and Response to Student Emotion. In: Nkambou, R., Bourdeau, J., Mizoguchi, R. (eds.) Advances in Intelligent Tutoring Systems. SCI, vol. 308, pp. 207–227. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Lehman, B.A., et al.: Inducing and Tracking Confusion with Contradictions During Complex Learning. IJAIED 22(2), 85–105 (2013)Google Scholar
  29. 29.
    Rai, D., Arroyo, I., Stephens, L., Lozano, C., Burleson, W., Woolf, B.P., Beck, J.E.: Repairing Deactivating Negative Emotions with Student Progress Pages. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 795–798. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luc Paquette
    • 1
  • Ryan S. J. D. Baker
    • 1
    • 2
  • Michael A. Sao Pedro
    • 2
  • Janice D. Gobert
    • 2
  • Lisa Rossi
    • 3
  • Adam Nakama
    • 2
  • Zakkai Kauffman-Rogoff
    • 2
  1. 1.Teachers CollegeColumbia UniversityNew YorkUSA
  2. 2.Worcester Polytechnic InstituteWorcesterUSA
  3. 3.Georgia Institute of TechnologyAtlantaUSA

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