Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game
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Published
May 1, 2013
Carol M. Forsyth
Arthur C. Graesser
Philip Pavlik Jr.
Zhiqiang Cai
Heather Butler
Diane Halpern
Keith Millis
Abstract
Operation ARIES! is an Intelligent Tutoring System that is designed to teach scientific methodology in a game-like atmosphere. A fundamental goal of this serious game is to engage students during learning through natural language tutorial conversations. A tight integration of cognition, discourse, motivation, and affect is desired to meet this goal. Forty-six undergraduate students from two separate colleges in Southern California interacted with Operation ARIES! while intermittently answering survey questions that tap specific affective and metacognitive states related to the game-like and instructional qualities of Operation ARIES!. After performing a series of data mining explorations, we discovered two trends in the log files of cognitive-discourse events that predicted self-reported affective states. Students reporting positive affect tended to be more verbose during tutorial dialogues with the artificial agents. Conversely, students who reported negative emotions tended to produce lower quality conversational contributions with the agents. These findings support a valence-intensity theory of emotions and also the claim that cognitive-discourse features can predict emotional states over and above other game features embodied in ARIES.
How to Cite
Forsyth, C. M., Graesser, A. C., Pavlik Jr., P., Cai, Z., Butler, H., Halpern, D., & Millis, K. (2013). Operation ARIES!: Methods, Mystery, and Mixed Models: Discourse Features Predict Affect in a Serious Game. Journal of Educational Data Mining, 5(1), 147–189. https://doi.org/10.5281/zenodo.3554615
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Keywords
motivation, intelligent tutoring systems, serious games, discourse, emotions, scientific inquiry skills, ARIES
References
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BAYLOR, A. L. AND KIM, Y. 2005. Simulating instructional roles through pedagogical agents. International Journal of Artificial Intelligence in Education 15, 95-115.
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CALVO, R. A., AND D'MELLO, S. K. 2010. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1, 18-37.
CERVONE, D. AND PALMER, B.W. 1990. Anchoring biases and the perseverance of self-efficacy beliefs. Cognitive Therapy and Research 14, 401-416.
CLORE, G.L. AND HUNTSINGER, J.R. 2007. How emotions inform judgment and regulate thought. Trends in Cognitive Science 11, 393-399.
CONATI, C., CHABBAL, R. & MACLAREN, H. 2003. A study on using biometric sensors for detecting user emotions in educational games. In Proceedings of the Workshop Assessing and Adapting to User Attitude and Affects: Why, When and How?.
CONATI, C. AND MACLAREN, H. 2009. Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19, 267-303.
CRAIG, S., D'MELLO, S., WITHERSPOON, A., AND GRAESSER, A.C. 2008. Emote aloud during learning with AutoTutor: Applying the facial action coding system to cognitive-affective states during learning. Cognition and Emotion 22, 777-788.
CRAIG, S., GRAESSER, A., SULLINS, J., AND GHOLSON, J. 2004. Affect and learning: An exploratory look into the role of affect in learning. Journal of Educational Media 29, 241-250.
CSIKSZENTMIHALYI, M. 1990. Flow: The Psychology of Optimal Experience. Harper-Row, New York, NY.
D'MELLO, S.K., CRAIG, S.D., AND GRAESSER, A.C. 2009. Multi-method assessment of affective experience and expression during deep learning. International Journal of Learning Technology 4, 165-187.
D'MELLO, S. AND GRAESSER, A.C. 2010. Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-adapted Interaction 20, 147-187.
D'MELLO, S. AND GRAESSER, A.C. 2012. Emotions during learning with AutoTutor. In P.J. DURLACH AND A. LESGOLD, Adaptive technologies for training and education. Cambridge University Press, England, UK.
D'MELLO, S., AND GRAESSER, A. in press. Dynamics of affective states during complex learning. Learning and Instruction.
D'MELLO,S., GRAESSER, A.C., AND STRAIN, A.C. in press. Emotions in advance learning technologies. In Handbook of Emotions and Education, R. PEKRUN AND LINNENBRING-GARCIA, Eds. Taylor and Francis Publishers, New York, NY.
DUNLOSKY, J., AND LIPKO, A. 2007. Metacomprehension: A brief history and how to improve its accuracy. Current Directions in Psychological Science 16, 228-232.
DWECK, C. S. 2002. Beliefs that make smart people dumb. In Why smart people do stupid things. R. J. Sternberg Ed. New Haven, CT: Yale University Press.
FREDERICKSON, B. L. 2001. The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist 56, 218–226.
FREDERICKSON, B.L. AND BRANIGAN, C. 2005. Positive emotions broaden the scope of attention and thought–action repertoires. Cognition and Emotion 19, 313–332.
GEE, J.P. 2003. What Video Games Teach Us about Language and Literacy. Palgrave/Macmillan, New York, NY.
GRAESSER, A. C., CHIPMAN, P., LEEMING, F., & BIEDENBACH, S. 2009. Deep learning and emotion in serious games. In Serious games: Mechanisms and effects, U. RITTERFELD, M. CODY, AND P. VORDERER, Eds. Taylor & Francis, Routledge: New York and London, 81-100.
D'MELLO, S. AND GRAESSER, A.C. in press. Language and discourse are powerful signals of student emotions during tutoring. IEEE Transactions on Learning Technologies.
GRAESSER, A.C. AND D'MELLO, S. 2012. Emotions during the learning of difficult material. In. B. ROSS Eds., The Psychology of Learning and Motivation 57, Elsevier, Amsterdam.
GRAESSER, A. C., D'MELLO, S. K., CRAIG, S. D., WITHERSPOON, A., SULLINS, J., MCDANIEL, AND GHOLSON, B. 2008. The relationship between affect states and dialogue patterns during interactions with AutoTutor. Journal of Interactive Learning Research 19, 293–312.
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