Towards sensor-free affect detection in cognitive tutor algebra

Proceedings of the 5th International Conference on Educational Data Mining, EDM 2012

Volumn , Issue , 2012, Pages

  Baker, Ryan S J D ^a   Gowda, Sujith M ^a   Wixon, Michael ^a   Kalka, Jessica ^a   Wagner, Angela Z ^b   Salvi, Aatish ^a,b   Aleven, Vincent ^b   Kusbit, Gail W ^b   Ocumpaugh, Jaclyn ^a   Rossi, Lisa ^a  

^a WORCESTER POLYTECHNIC INSTITUTE   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Affect detection; Affective computing; Boredom; Confusion; Educational data mining; Engaged concentration; Frustration; Intelligent tutoring system

Indexed keywords

ALGEBRA; COMPUTER AIDED INSTRUCTION; DATA MINING; EDUCATION COMPUTING; SEMANTICS;

AFFECT DETECTION; AFFECTIVE COMPUTING; BOREDOM; CONFUSION; EDUCATIONAL DATA MINING; FRUSTRATION; INTELLIGENT TUTORING SYSTEM;

STUDENTS;

EID: 85084011969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Arroyo, I., Woolf, B.P., Cooper, D., Burleson, W., Muldner, K., and Christopherson, R. Emotion Sensors Go To School. In V. Dimitrova, R. Mizoguchi, B. Du Boulay, and A. Graesser (eds.) 14th International Conference on Artificial Intelligence In Education, 2009.
2. Baker, R.S.J.d. Modeling and Understanding Students' Off-Task Behavior in Intelligent Tutoring Systems. Proceedings of ACM CHI 2007: Computer-Human Interaction, pp. 1059-1068, 2007.
3. Baker, R.S.J.d. Differences Between Intelligent Tutor Lessons, and the Choice to Go Off-Task. Proceedings of the 2nd International Conference on Educational Data Mining, pp. 11-20, 2009.
4. Baker, R.S.J.d., Corbett, A.T., and Aleven, V. More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, pp. 406-415, 2008.
5. Baker, R.S.J.d., Corbett, A.T., Koedinger, K.R., and Wagner, A.Z. Off-Task Behavior in the Cognitive Tutor Classroom: When Students "Game The System". Proceedings of ACM CHI 2004: Computer-Human Interaction, pp. 383-390, 2004.
6. Baker, R.S.J.d., Corbett, A.T., and Wagner, A.Z. Human Classification of Low-Fidelity Replays of Student Actions. Proceedings of the Educational Data Mining Workshop at the 8th International Conference on Intelligent Tutoring Systems, pp. 29-36, 2006.
7. Baker, R.S.J.d., D'Mello, S.K., Rodrigo, M.M.T., and 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), pp. 223-241, 2010.
8. Baker, R.S.J.d., and de Carvalho, A.M.J.A. Labeling Student Behavior Faster and More Precisely with Text Replays. Proceedings of the 1st International Conference on Educational Data Mining, pp. 38-47, 2008.
9. Baker, R.S.J.d., Goldstein, A.B., Heffernan, N.T. Detecting Learning Moment-by-Moment. To appear in International Journal of Artificial Intelligence in Education, in press.
10. Baker, R.S.J.d., and Gowda, S.M. An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools. Proceedings of the 3rd International Conference on Educational Data Mining, pp. 11-20, 2010.
11. Baker, R.S.J.d., Moore, G., Wagner, A., Kalka, J., Karabinos, M., Ashe, C., and Yaron, D. The Dynamics Between Student Affect and Behavior Occurring Outside of Educational Software. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction, pp. 14-24, 2011.
12. Baker, R.S.J.d., and Yacef, K. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), pp. 3-17, 2009.
13. Bartel, C.A., and Saavedra, R. The collective construction of work group moods. Administrative Science Quarterly, 45, pp. 197-231, 2001.
14. Calvo, R.A., and D’Mello, S.K. Affect Detection: An Interdisciplinary Review of Models, Methods, and their Applications. IEEE Transactions on Affective Computing, 1 (1), pp. 18-37, 2010.
15. Calvo, R.A., and D’Mello, S.K. eds. New Perspectives on Affect and learning technologies. New York: Springer. 2011.
16. th International Conference on Machine learning, pp. 108-114, 1995.
17. Cohen, J. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20 (1), pp. 37-46, 1960.
18. Conati, C., and Maclaren, H. Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction, 19, pp. 267-303, 2009.
19. D’Mello, S.K., Craig, S.D., Witherspoon, A. W., McDaniel, B. T., and Graesser, A. C. Automatic Detection of Learner’s Affect from Conversational Cues. User Modeling and User-Adapted Interaction, 18 (1-2), pp. 45-80, 2008.
20. D’Mello, S.K., and Graesser, A.C. Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-adapted Interaction. 20 (2), pp. 147-187, 2010.
21. D’Mello, S.K., Taylor, R., and Graesser, A.C. Monitoring Affective Trajectories during Complex Learning. In D.S. McNamara & J.G. Trafton (eds.) Proceedings of the 29th Annual Cognitive Science Society, pp. 203-208, 2007.
22. Dragon, T., Arroyo, I., Woolf, B.P., Burleson, W., El Kaliouby, R., and Eydgahi, H. Viewing Student Affect and Learning through Classroom Observation and Physical Sensors. International Conference on Intelligent Tutoring Systems 2008, pp. 29-39, 2008.
23. Hanley, J., and McNeil, B. The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology, 143, pp. 29-36, 1982.
24. Koedinger, K. R., Baker, R., Cunningham, K., Skogsholm, A., Leber, B., and Stamper, J. A data repository for the EDM community: The PSLC DataShop. In C. Romero, S. Ventura, M. Pechenizkiy, and R.S.J.D. Baker (eds.) Handbook of Educational Data Mining. pp. 43-55, Boca Raton, FL: CRC Press, 2010.
25. Koedinger, K.R., and Corbett, A.T. Cognitive Tutors: Technology bringing learning science to the classroom. In K. Sawyer (ed.) The Cambridge Handbook of the Learning Sciences, pp. 61-78, Cambridge, UK: Cambridge University Press, 2006.
26. Lee, D.M., Rodrigo, M.M., Baker, R.S.J.d., Sugay, J., and Coronel, A. Exploring the Relationship Between Novice Programmer Confusion and Achievement. Proceedings of the 4th bi-annual International Conference on Affective Computing and Intelligent Interaction, 2011.
27. Litman, D.J., and Forbes-Riley, K. Recognizing Student Emotions and Attitudes on the Basis of Utterances in Spoken Tutoring Dialogues with both Human and Computer Tutors. Speech Communication, 48 (5), pp. 559-590, 2006.
28. Muldner, K., Burleson, B., and VanLehn, K. “Yes!”: Using tutor and sensor data to predict moments of delight during instructional activities. Proceedings of the International Conference on User Modeling and Adaptive Presentation (UMAP’10), pp. 159-170, 2010.
29. Pardos, Z.A., and Heffernan, N. T. Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. To appear in the Journal of Machine Learning Research W & CP, in press.
30. Pekrun, R., Goetz, T., Titz, W., and Perry, R. P. Academic emotions in students’ self-regulated learning and achievement: A program of quantitative and qualitative research. Educational Psychologist, 37, pp. 91-106, 2002.
31. Planalp, S., DeFrancisco, V. L., and Rutherford, D. Varieties of cues to emotion in naturally occurring situations. Cognition and Emotion, 10 (2), pp. 137-153, 1996.
32. Rodrigo, M.M.T., Baker, R.S.J.d., D'Mello, S., Gonzalez, M.C.T., Lagud, M.C.V., Lim, S.A.L., Macapanpan, A.F., Pascua, S.A.M.S., Santillano, J.Q., Sugay, J.O., Tep, S., and Viehland, N.J.B. Comparing Learners' Affect While Using an Intelligent Tutoring Systems and a Simulation Problem Solving Game. Proceedings of the 9th International Conference on Intelligent Tutoring Systems, pp. 40-49, 2008.
33. Romero, C., and Ventura, S. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 6, pp. 601-618, 2010.
34. Sabourin, J., Mott, B., and Lester, J. Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks. Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, pp. 286-295, 2011.
35. Sabourin, J., Rowe, J., Mott, B., and Lester, J. When Off-Task in On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 534-536, 2011.
36. Shih, B., Koedinger, K.R., and Scheines, R. A response time model for bottom-out hints as worked examples. Proceedings of the 1st International Conference on Educational Data Mining, pp. 117-126, 2008.
37. Vuong, A., Nixon, T., and Towle, B. A Method for Finding Prerequisites Within a Curriculum. Journal of Educational Data Mining, pp. 211-216, 2011.
38. Yu, H.-F., Lo, H.-Y., Hsieh, H.-P., Lou, et al. Feature Engineering and Classifier Ensemble for KDD Cup 2010. Proceedings of the KDD Cup 2010 Workshop, pp. 1–16, 2010.