Abstract
Positive academic emotions are generally associated with positive learning experiences, while the opposite is true for negative emotions. This study examined changes in learners’ emotional profiles as they participated in MetaTutor, a computer-based learning environment designed to foster self-regulated learning via study of the human circulatory system. Latent transition analysis was employed to determine distinct, parsimonious emotional profiles over time. Learners are shown to move systematically among three profiles (positive, bored/frustrated, and moderate) in fairly predictable patterns. Of these, boredom is the most pressing concern given the relatively small chance of moving from boredom to a different emotional profile. Students’ learning gains were also significant predictors of emotional transitions. The findings suggest the need for timely intervention for learners who are on the verge of negative emotional trajectories, and the complex relationship between learning gains and emotions. In addition, latent transition analysis is demonstrated as a potentially useful technique for analyzing and utilizing multivariate panel data.
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References
Afzal, S., Robinson, P.: A study of affect in intelligent tutoring. In: Workshop on Modeling and Scaffolding Affective Experiences to Impact Learning, vol. 57, pp. 27–53 (2006)
Azevedo, R., Harley, J., Trevors, G., Feyzi-Behnagh, R., Duffy, M., Bouchet, F., Landis, R.S.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies, pp. 427–449. Springer, Amsterdam (2013). https://doi.org/10.1007/978-1-4419-5546-3_28
Azevedo, R., Martin, S.A., Taub, M., Mudrick, N.V., Millar, G.C., Grafsgaard, J.F.: Are pedagogical agents’ external regulation effective in fostering learning with intelligent tutoring systems? In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 197–207. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39583-8_19
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. Int. J. Hum.-Comput. Stud. 68(4), 223–241 (2010)
Carver, C.S., Harmon-Jones, E.: Anger is an approach-related affect: evidence and implications. Psychol. Bull. 135, 183–204 (2009)
Calvo, R.A., D’Mello, S. (eds.): New Perspectives on Affect and Learning Technologies. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-9625-1
Collins, L.M., Lanza, S.T.: Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. Wiley, Hoboken (2013)
Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Model. User-Adap. Inter. 19(3), 267–303 (2009)
Craig, S.D., Graesser, A.C., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. J. Educ. Media 29(3), 241–250 (2004)
D’Mello, S., Graesser, A.: The half-life of cognitive-affective states during complex learning. Cogn. Emot. 25(7), 1299–1308 (2011)
D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012)
Graesser, A.C., D’Mello, S.K., Strain, A.C.: Emotions in advanced learning technologies. In: International Handbook of Emotions in Education, pp. 473–493 (2014)
Jang, E.E., Lajoie, S.P., Wagner, M., Xu, Z., Poitras, E., Naismith, L.: Person-oriented approaches to profiling learners in technology-rich learning environments for ecological learner modeling. J. Educ. Comput. Res. 55(4), 552–597 (2017)
Jarrell, A., Harley, J.M., Lajoie, S.P.: The link between achievement emotions, appraisals, and task performance: pedagogical considerations for emotions in CBLEs. J. Comput. Educ. 3(3), 289–307 (2016)
Kapoor, A., Burleson, W., Picard, R.W.: Automatic prediction of frustration. Int. J. Hum. Comput. Stud. 65(8), 724–736 (2007)
Lau, C., Sinclair, J., Taub, M., Azevedo, R., Jang, E.E.: Transitioning self-regulated learning profiles in hypermedia-learning environments. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 198–202. ACM, March 2017
Pekrun, R.: The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ. Psychol. Rev. 18(4), 315–341 (2006)
Pekrun, R., Goetz, T., Frenzel-Anne, C., Petra, B., Perry, R.P.: Measuring emotions in students’ learning and performance: the achievement emotions questionnaire (AEQ). Contemp. Educ. Psychol. 36, 34–38 (2011)
Sam, Y.C., Ting, C.Y., Wong, C.O.: Extrapolating the role of affect into supporting conceptual change in scientific inquiry learning. In: 2012 International Conference on Information Retrieval & Knowledge Management (CAMP), pp. 158–161. IEEE (2012)
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Sinclair, J., Jang, E.E., Azevedo, R., Lau, C., Taub, M., Mudrick, N.V. (2018). Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds) Intelligent Tutoring Systems. ITS 2018. Lecture Notes in Computer Science(), vol 10858. Springer, Cham. https://doi.org/10.1007/978-3-319-91464-0_20
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