Abstract
This chapter provides a synthesis of several research methods used by interdisciplinary researchers to investigate emotions in advanced learning technologies. More specifically, the authors: (1) critique self-report measures used to investigate emotions; (2) briefly describe Scherer’s (2009) model as particularly relevant for investigating emotions due to its complex appraisal system; (3) describe three process-oriented methods (electrodermal activity, facial expressions, and eye-tracking) currently used by interdisciplinary researchers to detect, identify, and classify affective states during learning and briefly highlight strengths and weaknesses of each method; and (4) present major conceptual, methodological, and analytical issues related to investigating emotions during learning with advanced learning technologies.
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References
Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84–94.
Azevedo, R., & Aleven, V. (Eds.). (2013). International handbook of metacognition and learning technologies. Amsterdam, The Netherlands: Springer.
Azevedo, R., & Chauncey Strain, A. (2011). Integrating cognitive, metacognitive, and affective regulatory processes with MetaTutor. In R. Calvo & S. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 141–154). Amsterdam, The Netherlands: Springer.
Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 427–449). Amsterdam, The Netherlands: Springer.
Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253–274). Cambridge, MA: Cambridge University Press.
Barrett, L., & Russell, J. (2015). The psychological construction of emotion. New York, NY: Guilford.
Bondareva, D., Conati, C., Feyzi-Behnagh, R., Harley, J., Azevedo, R., & Bouchet, F. (2013). Inferring learning from gaze data during interaction with an environment to support self-regulated learning. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Lecture Notes in Computer Science, Vol. 7926. Artificial Intelligence in Education (pp. 229–238). Berlin, Germany: Springer.
Calvo, R., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18–37.
Calvo, R., D’Mello, S., Gratch, J., & Kappas, A. (Eds.). (2014). The Oxford handbook of affective computing. New York, NY: Oxford University Press.
Chauncey Strain, A., Azevedo, R., & D’Mello, S. (2013). Using a false biofeedback methodology to explore relationships among learners’ affect, metacognition, and performance. Contemporary Educational Psychology, 38, 22–39.
D’Mello, S. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105, 1082–1099.
D’Mello, S., & Graesser, A. (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., & Graesser, A. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 419–434). New York, NY: Oxford University Press.
Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46, 6–25.
Ekman, O., & Friesen, W. (1978). Facial action coding system: A technique for the measurement of facial movement. Palo Alto, CA: Consulting Psychologists Press.
Figner, B., & Murphy, R. (2011). Using skin conductance in judgment and decision making research. In M. Schulte-Mecklenbeck, A. Kuehberger, & R. Ranyard (Eds.), A handbook of process tracing methods for decision research. New York, NY: Psychology Press.
Graesser, A., & D’Mello, S. (2014). Emotions in advanced learning technologies. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 473–493). New York, NY: Routledge.
Graesser, A., D’Mello, S., & Chauncey Strain, A. (2014). Emotions in advanced learning technologies. In R. Pekrun & L. Linnenbrink-Garcia (Eds.), Handbook of emotions and education (pp. 473–493). New York, NY: Taylor & Francis.
Grafsgaard, J., Wiggins, J., Vail, A., Boyer, K., Wiebe, K., & Lester, J. (2014). The additive value of multimodal features for predicting engagement, frustration, and learning during tutoring. In Proceedings of the Sixteenth ACM International Conference on Multimodal Interaction (pp. 42–49).
Gratch, J., & Marsella, S. (2014). Appraisal models. In R. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 54–67). New York, NY: Oxford University Press.
Gross, J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26, 1–26.
Harley, J., Bouchet, F., Hussain, S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625.
Healey, J. (2014). Physiological sensing of emotion. In R. Calvo, S. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing (pp. 204–216). New York, NY: Oxford University Press.
Hot, P., Naveteur, J., Leconte, P., & Sequeira, H. (1999). Diurnal variations of tonic electrodermal activity. International Journal of Psychophysiology, 33, 223–230.
iMotions® Attention Tool (5.4.3) [Computer software]. (n.d.). Cambridge, MA: iMotions®.
Jaques, N., Conati, C., Harley, J., & Azevedo, R. (2014). Predicting affect from gaze data during interaction with an intelligent tutoring system. In S. Trausan-Matu, K. Boyer, M. Crosby, & K. Panourgia (Eds.), Proceedings of the 12th International Conference on Intelligent Tutoring Systems (ITS 2014) (pp. 29–38). Amsterdam, The Netherlands: Springer.
Lester, J., Mott, B., Robison, J., Rowe, J., & Shores, L. (2013). Supporting self-regulated science learning in narrative-centered learning environments. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 471–483). Amsterdam, The Netherlands: Springer.
Mudrick, N., Azevedo, R., Taub, M., Feyzi, R., & Bouchet, F. (2014). A study of subjective emotions, self-regulatory processes, and learning gains: Are pedagogical agents effective in fostering learning? In J. Polman et al. (Eds.), Proceedings of the International Conference of the Learning Sciences (pp. 309–316). Boulder, CO: ISLS.
Noldus FaceReader (6.0) [Computer software]. (n.d.). Wageningen, The Netherlands: Noldus Information Technology.
Papadopoulos, F., Corrigan, L., Jones, A., & Castellano, G. (2013). Learner modelling and automatic engagement recognition with robotic tutors. In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 740–744), IEEE.
Pekrun, R., Elliot, A., & Maier, M. (2006). Achievement goals and discrete achievement emotions: A theoretical model and prospective test. Journal of Educational Psychology, 98, 583–597.
Pekrun, R., & Linnenbrink-Garcia, L. (Eds.). (2014). Handbook of emotions and education. New York, NY: Taylor & Francis.
Pekrun, R., Goetz, T., Frenzel, A., Barchfeld, P., & Perry, R. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48.
Pour, P., Hussain, M., AlZoubi, O., D’Mello, S., & Calvo, R. (2010). The impact of system feedback on learners’ affective and physiological states. In Lecture Notes in Computer Science, Vol. 6094, Intelligent Tutoring Systems (pp. 264–273). New York, NY: Springer.
Scherer, K. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition and Emotion, 23, 1307–1351.
Scherer, K. (2013). The nature of dynamics of relevance and valence appraisals: Theoretical advances and recent evidence. Emotion Review, 5, 150–162.
Schutz, P., & Zembylas, M. (Eds.). (in press). Methodological advances in research on emotion and education. Amsterdam, The Netherlands: Springer.
Shi, Y., Ruiz, N., Taib, R., Choi, E., & Chen, F. (2007). Galvanic skin response (GSR) as an index of cognitive load. In Proceedings of CHI’07 Extended Abstracts on Human Factors in Computing System (pp. 2651–2656), ACM.
Taub, M., Azevedo, R., Bouchet, F., & Khosravifar, B. (2014). 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, 356–367.
Venableas, P., & Mitchell, D. (1996). The effects of age, sex and time of testing on skin conductance activity. Journal of Biological Psychology, 43, 87–101.
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The work in this chapter was supported in part by the National Science Foundation, Institute of Education Sciences, Social Sciences and Humanities Research Council of Canada, and Natural Sciences and Engineering Research Council of Canada.
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Azevedo, R., Taub, M., Mudrick, N., Farnsworth, J., Martin, S.A. (2016). Interdisciplinary Research Methods Used to Investigate Emotions with Advanced Learning Technologies. In: Zembylas, M., Schutz, P. (eds) Methodological Advances in Research on Emotion and Education. Springer, Cham. https://doi.org/10.1007/978-3-319-29049-2_18
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DOI: https://doi.org/10.1007/978-3-319-29049-2_18
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