Abstract
In this study we tested whether external regulation provided by artificial pedagogical agents (PAs) was effective in facilitating learners’ self-regulated learning (SRL) and can therefore foster complex learning with a hypermedia-based intelligent tutoring system. One hundred twenty (N = 120) college students learned about the human circulatory system with MetaTutor during a 2-hour session under one of two conditions: adaptive scaffolding (AS) or a control (C) condition. The AS condition received timely prompts from four PAs to deploy various cognitive and metacognitive SRL processes, and received immediate directive feedback concerning the deployment of the processes. By contrast, the C condition learned without assistance from the PAs. Results indicated that those in the AS condition gained significantly more knowledge about the science topic than those in the C condition. In addition, log-file data provided evidence of the effectiveness of the PAs’ scaffolding and feedback in facilitating learners’ (in the AS condition) metacognitive monitoring and regulation during learning. We discuss implications for the design of external regulation by PAs necessary to accurately detect, track, model, and foster learners’ SRL by providing more accurate and intelligent prompting, scaffolding, and feedback regarding SRL processes.
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
Notes
- 1.
The Bonferroni correction was used to adjust p values since several statistical tests were performed simultaneously on the data set.
References
Azevedo, R., Aleven, V. (eds.): International Handbook of Metacognition and Learning Technologies. Springer, Amsterdam (2013)
Winne, P.H., Azevedo, R.: Metacognition. In: Sawyer, K. (ed.) Cambridge Handbook of the Learning Sciences, 2nd edn, pp. 63–87. Cambridge University Press, Cambridge (2014)
D’Mello, S., Graesser, A.: Confusion and its dynamics during device comprehension with breakdown scenarios. Acta Psychol. 151, 106–116 (2014)
Kinnebrew, J., Segedy, J., Biswas, G.: Integrating model-driven and data-driven techniques for analyzing learning behaviors in open-ended learning environments. IEEE Transactions on Learning Technologies (in press). doi:10.1109/TLT.2015.2513387
Sabourin, J., Lester, J.: Affect and engagement in game-based learning environments. IEEE Trans. Affect. Comput. 5, 45–56 (2014)
Taub, M., Azevedo, R., Bouchet, F., Khosravifar, B.: Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments? Comput. Hum. Behav. 39, 356–367 (2014)
Azevedo, R.: Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning. Metacognition Learn. 9, 217–228 (2014)
Azevedo, R.: Defining and measuring engagement and learning in science: conceptual, theoretical, methodological, and analytical issues. Educ. Psychol. 50, 84–94 (2015)
Azevedo, R., Taub, M., Mudrick, N.V., Martin, S.A., Grafsgaard, J.F.: Understanding and reasoning about real-time cognitive, affective, metacognitive processes to foster self-regulation with advanced learning technologies. In: Schunk, D., Greene, J.A. (eds.) Handbook of Self-regulation and Performance, 2nd edn. Routledge, New York (in press)
Calvo, R., D’Mello, S.K. (eds.): New Perspectives on Affect and Learning Technologies. Springer, New York (2015)
Duffy, M., Azevedo, R.: Motivation matters: interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Comput. Hum. Behav. 52, 338–348 (2015)
Harley, J.M., Bouchet, F., Hussain, S., Azevedo, R., Calvo, R.: A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Comput. Hum. Behav. 48, 615–625 (2015)
Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., Landis, R.: 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)
Azevedo, R., Johnson, A., Chauncey, A., Graesser, A.: Use of hypermedia to convey and assess self-regulated learning. In: Zimmerman, B.J., Schunk, D.H. (eds.) Handbook of Self-regulation of Learning and Performance, pp. 102–121. Routledge, New York (2011)
Hadwin, A.F., Järvelä, S., Miller, M.: Self-regulated, co-regulated, and socially-shared regulation of learning. In: Zimmerman, B.J., Schunk, D.H. (eds.) Handbook of Self-regulation of Learning and Performance, pp. 65–84. Routledge, New York (2011)
VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46, 197–221 (2011)
Acknowledgements
This study was supported by funding from the National Science Foundation (DRL 1431552).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Azevedo, R., Martin, S.A., Taub, M., Mudrick, N.V., Millar, G.C., Grafsgaard, J.F. (2016). Are Pedagogical Agents’ External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems?. In: Micarelli, A., Stamper, J., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science(), vol 9684. Springer, Cham. https://doi.org/10.1007/978-3-319-39583-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-39583-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39582-1
Online ISBN: 978-3-319-39583-8
eBook Packages: Computer ScienceComputer Science (R0)