Abstract
The application of educational data mining (EDM) techniques to interactive learning software is increasingly being used to broaden the range of constructs typically incorporated in student models, moving from traditional assessment of student knowledge to the assessment of engagement, affect, strategy, and metacognition. Researchers are also broadening the range of environments within which these constructs are assessed. In this study, we develop sensor-free affect detection for EcoMUVE, an immersive multi-user virtual environment that teaches middle-school students about casualty in ecosystems. In this study, models were constructed for five different educationally-relevant affective states (boredom, confusion, delight, engaged concentration, and frustration). Such models allow us to examine the behaviors most closely associated with particular affective states, paving the way for the design of adaptive personalization to improve engagement and learning.
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References
AlZoubi, O., Calvo, R.A., Stevens, R.H.: Classification of EEG for affect recognition: An adaptive approach. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 52–61. Springer, Heidelberg (2009)
Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion Sensors Go To School. AIED, vol. 200 (2009)
Ivon, A., Woolf, B.P., Cooper, D.G., Burleson, W., Muldner, K.: The impact of animated pedagogical agents on girls’ and boys’ emotions, attitudes, behaviors and learning. In: 2011 11th IEEE International Conference on Advanced Learning Technologies, ICALT, pp. 506–510. IEEE (2011)
Baker, R.S.J.D., Gowda, S.M., Corbett, A.T., Ocumpaugh, J.: Towards automatically detecting whether student learning is shallow. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 444–453. Springer, Heidelberg (2012)
Baker, R.S.J.d., Mitrović, A., Mathews, M.: Detecting gaming the system in constraint-based tutors. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 267–278. Springer, Heidelberg (2010)
Baker, R.S.J.D., Gowda, S.M., Wixon, M., Kalka, J., Wagner, A.Z., Salvi, A., Aleven, V., Kusbit, G., Ocumpaugh, J., Rossi, L.: Towards Sensor-free Affect Detection in Cognitive Tutor Algebra. In: Proceedings of the 5th International Conference on Educational Data Mining, pp. 126–133 (2012)
Baker, R.S., 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)
Calvo, R.A., D’Mello, S.: Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1(1), 18–37 (2010)
Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media 29(3), 241–250 (2004)
Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19(3), 267–303 (2009)
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. User Modeling and User- Adapted Interaction 18(1-2), 45–80 (2008)
D’Mello, S., Graesser, A.: The half-life of cognitive-affective states during complex learning. Cognition & Emotion 25(7), 1299–1308 (2011)
Hanley, J., McNeil, B.: The Meaning and Use of the Area under a Receiver Operating Characteristic (ROC) Curve. Radiology 143, 29–36 (1982)
Ketelhut, D.J., et al.: A multi‐user virtual environment for building and assessing higher order inquiry skills in science. British Journal of Educational Technology 41(1), 56–68 (2010)
Lehman, B.A., D’Mello, S.K., Strain, A., Millis, C., Gross, M., Dobbins, A., Wallace, P., Millis, K., Graesser, A.C.: Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education 22(2), 85–105 (2013)
Litman, D.J.: 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)
Liu, Z., Pataranutaporn, V., Ocumpaugh, J., Baker, R.S.: Sequences of Frustration and Confusion, and Learning. In: Proceedings of the 6th International Conference on Educational Data Mining, pp. 114–120 (2013)
Metcalf, S.J., Kamarainen, A., Grotzer, T., Dede, C.: Ecosystem science learning via multi-user virtual environments. International Journal of Gaming and Computer-Mediated Simulations 3(1), 86 (2011)
Ocumpaugh, J., Baker, R.S.J.D., Rodrigo, M.A.: Quantitative Field Observation (QFOs) Baker-Rodrigo Observation Method Protocol (BROMP) 1.0 Training Manual version 1.0 (October 17, 2012)
Pantic, M., Leon, J.M.: Rothkrantz. Toward an affect-sensitive multimodal human-computer interaction. Proceedings of the IEEE 91(9), 1370–1390 (2003)
Pardos, Z.A., Baker, R.S., San Pedro, M.O., 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 (2013)
Planalp, S., DeFrancisco, V.L., Rutherford, D.: Varieties of Cues to Emotion in Naturally Occurring Settings. Cognition and Emotion 10(2), 137–153 (1996)
Rodrigo, M.M.T., Baker, R.S.J.D.: Comparing Learners’ Affect While Using an Intelligent Tutor and an Educational Game. Research and Practice in Technology Enhanced Learning 6(1), 43–66 (2011)
Rodrigo, M.M.T., Baker, R.S.J.D.: Coarse-Grained Detection of Student Frustration in an Introductory Programming Course. In: Proceedings of ICER 2009: the International Computing Education Workshop (2009)
Sabourin, J., Mott, B., Lester, J.C.: 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)
San Pedro, M.O.Z., Baker, R.S.J.D., Bowers, A.J., Heffernan, N.T.: Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School. In: Proceedings of the 6th International Conference on Educational Data Mining, pp. 177–184 (2013)
San Pedro, M.O.Z., Baker, R.S.J.d., Gowda, S.M., Heffernan, N.T.: Towards an understanding of affect and knowledge from student interaction with an intelligent tutoring system. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 41–50. Springer, Heidelberg (2013)
Sebe, N., Cohen, I., Gevers, T., Huang, T.S.: Multimodal approaches for emotion recognition: a survey. In: International Society for Optics and Photonics Electronic Imaging 2005, pp. 56–67 (2005)
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(1), 39–58 (2009)
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Baker, R.S., Ocumpaugh, J., Gowda, S.M., Kamarainen, A.M., Metcalf, S.J. (2014). Extending Log-Based Affect Detection to a Multi-User Virtual Environment for Science. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_25
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DOI: https://doi.org/10.1007/978-3-319-08786-3_25
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