Abstract
Affect and metacognition play a central role in learning. We examine the relationships between students’ affective state dynamics, metacognitive judgments, and performance during learning with MetaTutorIVH, an advanced learning technology for human biology education. Student emotions were tracked using facial expression recognition embedded within MetaTutorIVH and transitions between emotions theorized to be important to learning (e.g., confusion, frustration, and joy) are analyzed with respect to likelihood of occurrence. Transitions from confusion to frustration were observed at a significantly high likelihood, although no differences in performance were observed in the presence of these affective states and transitions. Results suggest that the occurrence of emotions have a significant impact on students’ retrospective confidence judgments, which they made after submitting their answers to multiple-choice questions. Specifically, the presence of confusion and joy during learning had a positive impact on student confidence in their performance while the presence of frustration and transition from confusion to frustration had a negative impact on confidence, even after accounting for individual differences in multiple-choice confidence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instruct. 22(2), 145–157 (2012)
Baker, R., D’Mello, S., Rodrigo, M.M.T., Graesser, A.: 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)
Lallé, S., Conati, C., Carenini, G.: Predicting confusion in information visualization from eye tracking and interaction data. In: International Joint Conference on Artificial Intelligence, pp. 2529–2535 (2016)
Boulay, B., Centred, H.: Towards systems that care: a conceptual framework based on motivation, metacognition and sffect. Int. J. Artif. Intell. Educ. 20(3), 197–229 (2010)
D’Mello, S., Graesser, A.: Feeling, thinking, and computing with affect aware learning. In: Calvo, R., D’Mello, S., Gratch, J., Kappas, A. (eds.) The Oxford Handbook on Affective Computing, pp. 419–434. Oxford Library of Psychology (2014)
Azevedo, R., Mudrick, N., Taub, M., Wortha, F.: Coupling between metacognition and emotions during STEM learning with advanced learning technologies: a critical analysis, implications for future research, and design of learning systems. In: Michalsky, T., Schechter, C. (eds.) Self-regulated Learning: Conceptualization, Contribution, and Empirically Based Models for Teaching and Learning. Teachers College Press, New York (2017)
Ekman, P., Friesen, W.V.: Measuring facial movement. Environ. Psychol. Nonverbal Behav. 1(1), 56–75 (1976)
Calvo, R., Mello, S.: Affect detection: an interdisciplinary review of model, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)
Botelho, A.F., Baker, R.S., Heffernan, N.T.: Improving sensor-free affect detection using deep learning. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 40–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_4
Sawyer, R., Smith, A., Rowe, J., Azevedo, R., Lester, J.: Enhancing student models in game-based learning with facial expression recognition. In: Proceedings of the Twenty-Fifth Conference on User Modeling, Adaptation, and Personalization, pp. 192–201. ACM, Bratislava, Slovakia (2017)
Ekman, P., Friesen, W.: Facial action coding system (1977)
Bates, D., Mächler, M., Bolker, B.M., Walker, S.C.: Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015)
iMotions Biometric Research Platform 6.0, iMotions A/S, Copenhagen, Denmark (2016)
Acknowledgements
This research was supported by funding from the National Science Foundation (DRL #1431552). The authors would also like to thank members of the SMART Lab and IntelliMedia Group for their contributions to this project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sawyer, R., Mudrick, N.V., Azevedo, R., Lester, J. (2018). Impact of Learner-Centered Affective Dynamics on Metacognitive Judgements and Performance in Advanced Learning Technologies. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-93846-2_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93845-5
Online ISBN: 978-3-319-93846-2
eBook Packages: Computer ScienceComputer Science (R0)