Abstract
Research on scaffolding students’ self-regulation and metacognition during learning with advanced learning technologies has led to numerous models and theories from various fields. The role of students’ emotional and motivational processes, on the other hand, has recently become a central focus of research. In this chapter, we describe recent findings regarding emotions during learning, in classroom-based and laboratory-based research domains. Next, we discuss how these findings influenced the development of MetaTutor, a research tool and learning environment, designed to track students’ emotions during learning. We present results from a preliminary investigation into MetaTutor’s usefulness for tracking students’ emotions during a 2-h learning session. We conclude by discussing implications for incorporating affect into models of self-regulated learning, and the development of computer-based learning environments that track, scaffold, and respond to students’ emotions during learning.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Astleitner, H., & Leutner, D. (2000). Designing instructional technology from an emotional perspective. Journal of Research on Computing in Education, 32, 497–510.
Azevedo, R. (2008). The role of self-regulated learning about science with hypermedia. In D. Robinson & G. Schraw (Eds.), Recent innovations in educational technology that facilitate student learning (pp. 127–156). Charlotte, NC: Information Age Publishing.
Azevedo, R. (2009). Theoretical, methodological, and analytical challenges in the research on metacognition and self-regulation: A commentary. Metacognition & Learning, 4, 87–95.
Azevedo, R., Johnson, A., Burkett, C., Chauncey, A., Lintean, M., & Rus, V. (2010). The role of prompting and feedback in facilitating students’ learning about science with MetaTutor. Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence, Arlington, VA.
Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In M. Khine & I. Saleh (Eds.), New science of learning: Computers, cognition, and collaboration in education (pp. 225–247). Amsterdam: Springer.
Azevedo, R., Moos, D., Johnson, A., & Chauncey, A. (2010). Measuring the cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45, 210–223.
Azevedo, R., Moos, D., Witherspoon, A., & Chauncey, A. (2010). Measuring cognitive and metacognitive regulatory processes used during hypermedia learning: Issues and challenges. Educational Psychologist, 45(4), 210–223.
Azevedo, R., & Witherspoon, A. M. (2009). Self-regulated learning with hypermedia. In A. Graesser, J. Dunlosky, D. Hacker (Eds.), Handbook of metacognition in education (pp. 319–339). Mahwah, NJ: Erlbaum.
Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18–37.
Chauncey, A., & Azevedo, R. (2010). Emotions and motivation during multimedia learning: How do I feel and why do I care? In V. Aleven, J. Kay, & J. Mostow (Eds.), ITS 2010, Part 1, LNCS 6094, (pp.369–378).
Church, M. A., Elliot, A. J., & Gable, S. L. (2001). Perceptions of classroom environment, achievement goals, and achievement outcomes. Journal of Educational Psychology, 93, 43–54.
Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004). Affect and learning: An exploratory look into the role of affect in learning. Journal of Educational Media, 29, 241–250.
D’Mello, S. K., Craig, S., & Graesser, A. (2009). Multi-method assessment of affective experience and expression during deep learning. International Journal of Learning Technology, 4, 165–187.
D’Mello, S., & 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(2), 147–187.
D’Mello, S., Lehman, B., & Graesser, A. (2011). A motivationally supportive affect-sensitive AutoTutor. In R. Calvo & S. D’Mello (Eds.), Affective prospecting (Explorations in the learning sciences, instructional systems and performance). New York: Springer.
D’Mello, S. K., Taylor, R., & Graesser, A. C. (2007). Monitoring affective trajectories during complex learning. In D. S. McNamara & J. G Trafton (Eds.), Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 203–208). Austin, TX: Cognitive Science Society.
Efklides, A. & Volet, S. (Eds.). (2005). Feelings and emotions in the learning process. Learning and Instruction, 15 (5) [Whole issue].
Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6, 169–200.
Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005). AutoTutor: An intelligent tutoring system with mixed-initiative dialog. Transactions on Education, 48, 612–618.
Graesser, A. C., Lu, S., Olde, B. A., Cooper-Pye, E., & Whitten, S. (2005). Question asking and eye-tracking during cognitive disequilibrium: Comprehending illustrated texts on devices when the devices break down. Memory and Cognition, 7, 1235–1247.
Graesser, A. C., McDaniel, B., Chipman, P., Witherspoon, A., D’Mello, S., & Gholson, B. (2006).
Detection of emotions during learning with AutoTutor. Proceedings of the 28th Annual Conference of the Cognitive Science Society (pp. 285–290). Washington, DC: Cognitive Science Society.
Gross, J. J. (Ed.). (2007). Handbook of emotion regulation. New York, NY: Guilford Press.
Gross, J. J. (2008). Emotion regulation. In M. Lewis, J. M. Haviland-Jones, & L. F. Barrett (Eds.), Handbook of emotions (3rd ed., pp. 497–512). New York, NY: Guilford.
Koole, S. (2009). The psychology of emotion regulation: An integrative review. Cognition & Emotion, 23, 4–41.
Kort, B., Reilly, R., & Picard, R. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy–building a learning companion. In T. Okamato, R. Hartley, Kinshuk & J. P. Klus (Eds.), Proceedings IEEE International Conference on Advanced Learning Technologies: Issues, Achievements, and Challenges (pp. 43–38). Madison, Wisconsin: IEEE Computer Society.
Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: The Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18, 181–208.
Lehman, B., D’Mello, S. K., Strain, A. C., Gross, M., Dobbins, A., Wallace, P., et al. (in press). Inducing and tracking confusion with contradictions during critical thinking and scientific reasoning. In S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education. New York / Heidelber: Springer.
Lester, J. C., McQuiggan, S. W., & Sabourin, J. L. (2011). Affect recognition and expression in narrative-centered learning environments. In R. Calvo & S. D’Mello (Eds.), Affective prospecting (Explorations in the learning sciences, instructional systems and performance). New York: Springer.
McQuiggan, S., Robison, J., & Lester, J. (2010). Affective transitions in narrative-centered learning environments. Educational Technology & Society, 13, 40–53.
Moos, D. C., & Marroquin, L. (2010). Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered. Computers in Human Behavior, 26, 265–276.
Pekrun, R., Elliot, A. J., & Maier, M. A. (2006). Achievement goals and achievement emotions: A theoretical model and prospective test. Journal of Educational Psychology, 98, 583–597.
Pekrun, R., Goetz, T., Daniels, L., Stupnisky, R., & Perry, R. (2010). Boredom in achievement settings: Exploring control antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102, 531–549.
Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of quantitative and qualitative research. Educational Psychologist, 37, 91–106.
Pekrun, R., Maier, M. A., & Elliot, A. J. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101, 115–135.
Schutz, P. A., & Davis, H. A. (2000). Emotions during self regulation: The regulation of emotion during test taking. Educational Psychologist, 35, 243–256.
Schutz, P. A., & DeCuire, J. T. (2002). Inquiry on emotions in education. Educational Psychologist, 37, 125–134.
Vansteenkiste, M., Simons, J., Lens, W., Soenens, B., & Matos, L. (2005). Examining the motivational impact of intrinsic versus extrinsic goal framing and autonomy-supportive versus internally controlling communication style on early adolescents’ academic achievement. Child Development, 2, 483–501.
Veenman, M. (2007). The assessment and instruction of self-regulation in computer-based environments: A discussion. Metacognition and Learning, 2, 177–183.
White, B., Frederiksen, J., & Collins, A. (2009). The interplay of scientific inquiry and metacognition: More than a marriage of convenience. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 175–205). New York: Routledge.
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition and educational theory and practice (pp. 277–304). Hillsdale, NJ: Erlbaum.
Winne, P., & Hadwin, A. (2008). The weave of motivation and self-regulated learning. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297–314). NY: Taylor & Francis.
Winne, P. H., & Nesbit, J. C. (2009). Supporting self-regulated learning with cognitive tools. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 259–277). New York: Routledge.
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009). A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transaction on Pattern Analysis and Machine Intelligence, 31, 39–58.
Zimmerman, B. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, CA: Academic.
Zimmerman, B. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166–183.
Acknowledgments
The research presented in this chapter has been supported by funding from the National Science Foundation (Early Career Grant DRL 0133346, DRL 0633918, DRL 0731828, HCC 0841835) awarded to the first author. The authors would also like to thank Candice Burkett, Michael Cox, Eric Brooks, Andrew Hoff, and Rachel Anderson for the data collection, and Amy Johnson, Mihain Lintean, Z. Cai, Vasile Rus, Art Graesser, and Danielle McNamara for the design and development of MetaTutor. Current work on the MetaTutor is funded by a grant from the National Science Foundation (DRL 1008282) awarded to the first author and his colleagues, Ronald Landis and Mohammed Yeasin, at the University of Memphis.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Azevedo, R., Strain, A.C. (2011). Integrating Cognitive, Metacognitive, and Affective Regulatory Processes with MetaTutor. In: Calvo, R., D'Mello, S. (eds) New Perspectives on Affect and Learning Technologies. Explorations in the Learning Sciences, Instructional Systems and Performance Technologies, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9625-1_11
Download citation
DOI: https://doi.org/10.1007/978-1-4419-9625-1_11
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9624-4
Online ISBN: 978-1-4419-9625-1
eBook Packages: Humanities, Social Sciences and LawEducation (R0)