A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system

Computers in Human Behavior

Volumn 48, Issue , 2015, Pages 615-625

  Harley, Jason M ^a,b   Bouchet, François ^c,d   Hussain, M Sazzad ^e   Azevedo, Roger ^f   Calvo, Rafael ^e  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b MCGILL UNIVERSITY   (Canada)

^c SORBONNE UNIVERSITÉ   (France)

^d UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

^e UNIVERSITY OF SYDNEY   (Australia)

^f NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

Affect; Computer based learning environments; Emotions; Intelligent tutoring systems (ITS)

Indexed keywords

COMPUTER AIDED INSTRUCTION; HUMAN COMPUTER INTERACTION; INTELLIGENT AGENTS; LEARNING SYSTEMS; MULTI AGENT SYSTEMS;

AFFECT; AUTOMATIC FACIAL EXPRESSION RECOGNITION; COMPUTER-BASED LEARNING ENVIRONMENTS; ELECTRODERMAL ACTIVITY; EMOTIONS; INTELLIGENT MULTI AGENT SYSTEMS; INTELLIGENT TUTORING SYSTEM; LEARNING ENVIRONMENTS;

FACE RECOGNITION;

EID: 84930011314     PISSN: 07475632     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chb.2015.02.013     Document Type: Article

References (53)

1. Alzoubi, O., Hussain, M. S., D'Mello, S., & Calvo, R. A. (2011). Affective modeling from multichannel physiology: Analysis of day differences. In S. D'Mello, A. Graesser, B. Schuller, J.-C. Martin (Eds.), Lecture notes in computer science: Vol: 6974. Affective computing and intelligent interaction (pp. 4-13). Berlin, Heidelberg: Springer-Verlag.
2. I. Arroyo, B. Woolf, D. Cooper, W. Burleson, K. Muldner, and R. Christopherson Emotion sensors go to school V. Dimitrova, R. Mizoguchi, B. du Boulay, A. Graesser, Proceedings of the international conference on artificial intelligence in education 2009 IOS Press Amsterdam, Netherlands 17 24
3. R. Azevedo, and V. Aleven International handbook of metacognition and learning technologies 2013 Springer Amsterdam, The Netherlands
4. R. Azevedo, R. Behnagh, M. Duffy, J. Harley, and G. Trevors Metacognition and self regulated learning with advanced learning technologies D. Jonassen, S. Land, Theoretical foundations of learning environments 2nd ed. 2012 Erlbaum Mahwah, NJ 171 197
5. R. Azevedo, J. Harley, G. Trevors, R. Feyzi-Behnagh, M. Duffy, and F. Bouchet Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems R. Azevedo, V. Aleven, International handbook of metacognition and learning technologies 2013 Springer-Verlag Amsterdam, The Netherlands 427 449
6. R.S.D. Baker, S.M. Gowda, M. Wixon, J. Kalka, A.Z. Wagner, and A. Salvi Towards sensor-free affect detection in cognitive tutor algebra K. Yacef, O. Zaiane, H. Hershkovitz, J. Stamper, Proceedings of the 5th international conference on educational data mining 2012 International Educational Data Mining Society Crete, Greece 126 133
7. R. Baker, M. Rodrigo, and U. Xolocotzin The dynamics of affective transitions in simulation problem-solving environments A.R. Paiva, R. Prada, R. Picard, Affective Computing and Intelligent Interaction Vol. 4738 2007 Springer Berlin, Heidelberg 666 677
8. R.A. Calvo, and S. D'Mello New perspectives on affect and learning technologies 2011 Springer New York
9. Y.E. Chentsova-Dutton, and J.L. Tsai Self-focused attention and emotional reactivity: The role of culture Journal of Personality and Social Psychology 98 2010 507 519
10. S.K. D'Mello, R. Dale, and A. Graesser Disequilibrium in the mind, disharmony in the body Cognition and Emotion 22 2012 362 374
11. S.K. D'Mello, and A. Graesser Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features User Modeling and User-Adapted Interaction 20 2 2010 147 187
12. S.K. D'Mello, and A.C. Graesser Dynamics of affective states during complex learning Learning and Instruction 22 2012 145 157
13. S.K. D'Mello, and J. Kory Consistent but modest: A meta-Analysis on unimodal and multimodal affect detection accuracies from 30 studies G. Castellano, K. Masse, Proceedings of the 14th ACM international conference on multimodal interaction 2012 ACM New York 31 38
14. S.K. D'Mello, B. Lehman, and N. Person Monitoring affective states during effortful problem solving activities International Journal of Artificial Intelligence in Education 20 2010 361 389
15. P. Ekman An argument for basic emotions Cognition and Emotion 6 1992 169
16. FaceReader (5.0) [Computer software]. Wageningen, The Netherlands: Noldus Information Technology.
17. A.C. Graesser, S.K. D'Mello, and A. Strain Emotions in advanced learning technologies R. Pekrun, L. Linnenbrink-Garcia, Handbook of emotions and education 2014 Taylor & Francis New York, NY 473 493
18. J.F. Grafsgaard, J.B. Wiggins, K.E. Boyer, E.N. Wiebe, and J.C. Lester Predicting learning and affect from multimodal data streams in task-oriented tutorial dialogue J. Stamper, Z. Pardos, M. Mavrikis, B.M. McLaren, Proceedings of the 7th international conference on educational data mining 2014 International Educational Data Mining Society London, England 122 129
19. J.J. Gross The future's so bright, I gotta wear shades Emotion Review 2 2010 212 216
20. J.J. Gross Emotion regulation: Taking stock and moving forward Emotion 13 2013 359 365
21. J.J. Gross, G. Sheppes, and H.L. Urry Emotion generation and emotion: A distinction we should make (carefully) Cognition and Emotion 25 2011 765 781
22. Harley, J. M. (in press). Measuring emotions: A survey of cutting-edge methodologies used in advanced agent-based learning environment research. In S. Tettegah & M. Gartmeier (Eds.). Emotions, technology and learning: Communication of feeling for, with, and through digital media (Vol.1: Emotions, Technology, Learning and Design). London, UK: Elsevier.
23. J.M. Harley, and R. Azevedo Toward a feature-driven understanding of students' emotions during interactions with agent-based learning environments: A selective review International Journal of Games and Computer Mediated Simulation 6 3 2014 17 34
24. Harley, J., Bouchet, F., & Azevedo, R. (2012). Measuring learners' co-occurring emotional responses during their interaction with a pedagogical agent in MetaTutor. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Lecture notes in computer science: Vol: 7315. Intelligent tutoring systems (pp. 40-45). Berlin, Heidelberg: Springer-Verlag.
25. Harley, J. M., Bouchet, F., & Azevedo, R. (2013). Aligning and comparing data on learners' emotions experienced with MetaTutor. In C. H. Lane, K. Yacef, J. Mostow, P. Pavik (Eds.), Lecture notes in computer science: 7926. Artificial intelligence in education (pp. 61-70). Berlin, Heidelberg: Springer-Verlag.
26. Kapoor, A, & Picard, R. W. (2005). Multimodal affect recognition in learning environments. In Proceedings of the 13th annual ACM international conference on multimedia (pp. 677-682). New York, NY: ACM.
27. A. Kapoor, W. Burleson, and R.W. Picard Automatic prediction of frustration International Journal of Human-Computer Studies 65 2007 724 736
28. I.B. Mauss, R.W. Levenson, L. McCarter, F.H. Wilhelm, and J.J. Gross The tie that binds? Coherence among emotion experience, behavior, and physiology Emotion 5 2 2005 175 190
29. I.B. Mauss, and M.D. Robinson Measures of emotion: A review Cognition and Emotion 23 2009 209 237
30. McDaniel, B. T., D'Mello, S. K., King, B. G., Chipman, P., Tapp, K., & Graesser, A. C. (2007). Facial features for affective state detection in learning environments. In D. McNamara & G. Trafton (Eds.), Proceedings of the 29th annual cognitive science society (pp. 467-472). New York, NY: Erlbaum.
31. S.W. McQuiggan, J.L. Robison, and J.C. Lester Affective transitions in narrative-centered learning environments Journal of Educational Technology & Society 13 1 2010 40 53
32. O. AlZoubi, S.K. D'Mello, and R.A. Calvo Detecting naturalistic expressions of nonbasic affect using physiological signals IEEE Transactions on Affective Computing 3 2012 298 310
33. M. Pantic, and L.J.M. Rothkrantz Toward an affect-sensitive multimodal human-computer interaction Proceedings of the IEEE 91 9 2003 1370 1390
34. R. Pekrun The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice Educational Psychology Review 18 2006 315 341
35. Pekrun, R., & Linnenbrink-Garcia, L. (Eds.). (2014) Handbook of emotions in education. New York, NY: Routledge.
36. R. Pekrun Emotions as drivers of learning and cognitive development R.A. Calvo, S. D'Mello, New perspectives on affect and learning technologies 2011 Springer New York 23 39
37. R. Pekrun, T. Goetz, C. Frenzel-Anne, B. Petra, and R.P. Perry Measuring emotions in students' learning and performance: The Achievement Emotions Questionnaire (AEQ) Contemporary Educational Psychologist 36 2011 34 48
38. R. Pekrun, T. Goetz, W. Titz, and R. Perry Academic achievement emotions in students' self-regulated learning and achievement: A program of quantitative and qualitative research Educational Psychologist 37 2002 91 206
39. R. Pekrun, and P.P. Perry Control-value theory of achievement emotions R. Pekrun, L. Linnenbrink-Garcia, International handbook of emotions in education 2014 Routledge New York 120 141
40. P. Pintrich The role of goal orientation in self-regulated learning M. Boekaerts, P. Pintrich, M. Zeidner, Handbook of self-regulation 2000 Academic Press San Diego, CA 451 502
41. Q-Sensor 2.0 [Apparatus and software]. (2013). Waltham, MA: Affectiva.
42. J.A. Russell, A. Weiss, and G.A. Mendelsohn Affect grid: A single-item scale of pleasure and arousal Journal of Personality and Social Psychology 57 3 1989 493 502
43. J. Sabourin, and J. Lester Affect and engagement in game-based learning environments IEEE Transactions on Affective Computing 5 1 2014 45 56
44. B.G. Tabachnick, and L.S. Fidell Using multivariate statistics 5th ed. 2007 Pearson Education/Allyn and Bacon Boston, MA
45. M. Taub, R. Azevedo, F. Bouchet, and B. Khosravifar Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments? Computers in Human Behavior 39 2014 356 367
46. V. Terzis, C.N. Moridis, and A.A. Economides Measuring instant emotions during a self-assessment test: The use of FaceReader A.J. Spink, F. Grieco, O.E. Krips, L.W.S. Loijens, L.P.J.J. Noldus, P.H. Zimmerman, Proceedings of Measuring Behavior 2010 ACM Eindoven, The Netherlands 192 195
47. G. Trevors, M. Duffy, and R. Azevedo Note-taking within MetaTutor: Interactions between an intelligent tutoring system and prior knowledge on note-taking and learning Educational Technology Research and Development 2014 1 22
48. M. Utthara, S. Suranjana, D. Sukhendu, and C. Pinaki A survey of decision fusion and feature fusion strategies for pattern classification IETE Technical Review 27 4 2010 293 307
49. Wagner, J., Kim, J., & André, E. (2005, July). From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In IEEE international conference on multimedia and expo (pp. 940-943). Amsterdam, The Netherlands: IEEE Press.
50. H.G. Wallbott Bodily expression of emotion European Journal of Social Psychology 28 6 1998 879 896
51. P.H. Winne, and A.F. Hadwin NStudy: Tracing and supporting self-regulated learning in the internet R. Azevedo, V. Aleven, International handbook of metacognition and learning technologies 2013 Springer-Verlag Amsterdam, The Netherlands 293 308
52. B. Woolf, W. Burleson, I. Arroyo, T. Dragon, D. Cooper, and R. Picard Affect-aware tutors: Recognizing and responding to student affect International Journal of Learning Technology 4 2009 129 164
53. B. Zimmerman, and D. Schunk Handbook of self-regulation of learning and performance 2011 Routledge New York