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23 - Self-Regulation in Computer-Assisted Learning Systems

from Part V - Metacognition

Published online by Cambridge University Press:  08 February 2019

John Dunlosky
Affiliation:
Kent State University, Ohio
Katherine A. Rawson
Affiliation:
Kent State University, Ohio
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Summary

Computer-Assisted Learning Systems (CALSs) have the potential to transform learning by supporting and augmenting students’ ability to accurately monitor and regulate key cognitive, affective, metacognitive, motivational and social processes. Recent advances in the cognitive, learning, computational, and engineering sciences make is possible to significantly augment existing CALSs both as research tools (e.g., examine temporally unfolding self-regulatory processes) as well as instructional tools (e.g., foster metacognitive skills). The goal of this chapter is to present research on self-regulation in CALSs by providing examples from contemporary systems and also how we use multimodal multichannel data (e.g., log files, eye tracking, facial expressions of emotions, physiological sensors, concurrent verbalizations) to examine cognitive, affective, and metacognitive (CAM) self-regulatory processes in these systems. As such, we first provide a brief history of research in SRL with CALSs and discuss how different CALSs have been used to study and foster SRL. We will then present and discuss conceptual and theoretical issues derived from several models, frameworks, and theories of SRL that focus on CAM processes. Following this, we discuss several dichotomies related to CAM and the challenges they pose for the measurement and support of SRL with CALSs. Lastly, we present challenges and future directions that need to be addressed by interdisciplinary researchers to advance the field of SRL and CALSs.
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Publisher: Cambridge University Press
Print publication year: 2019

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References

Aleven, V. & Koedinger, K. (2016). An interview reflection on “Intelligent tutoring goes to school in the big city.” International Journal of Artificial Intelligence in Education, 26, 1324.Google Scholar
Arguel, A., Lockyer, L., Lipp, O. V., Lordge, J. M., & Kennedy, G. (2016). Inside out: Detecting learners’ confusion to improve interactive digital learning environments. Journal of Educational Computing Research, 55, 526551.Google Scholar
Azevedo, R. (2014). Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning. Metacognition and Learning, 9, 217228.Google Scholar
Azevedo, R. & Aleven, V. (eds.). (2013). International handbook of metacognition and learning technologies. Amsterdam: Springer.Google Scholar
Azevedo, R. & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96, 523535.Google Scholar
Azevedo, R., Feyzi-Behnagh, R., Duffy, M., Harley, J., & Trevors, G. (2012). Metacognition and self-regulated learning in student-centered learning environments. In Jonassen, D. & Land, S. (eds.), Theoretical foundations of student-center learning environments, 2nd edn (pp. 171197). New York: Routledge.Google Scholar
Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. S. (2013). 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. 427449). Amsterdam: Springer.CrossRefGoogle Scholar
Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010). Self-regulated learning with MetaTutor: Advancing the science of learning with metacognitive tools. In Khine, M. & Saleh, I. (eds.), New science of learning (pp. 225247). New York: Springer.Google Scholar
Azevedo, R., Mudrick, N. V., Taub, M., & Wortha, F. (2017). 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 (pp. 118). New York: Teachers College Press.Google Scholar
Azevedo, R., Taub, M., & Mudrick, N. V. (2018). Using multi-channel trace data to infer and foster self-regulated learning between humans and advanced learning technologies. In Schunk, D. & Greene, J. A. (eds.), Handbook of self-regulation of learning and performance, 2nd edn (pp. 254270). New York: Routledge.Google Scholar
Azevedo, R. & Witherspoon, A. M. (2009). Self-regulated learning with hypermedia. In Hacker, D. J., Dunlosky, J., & Graesser, A. C. (eds.), Handbook of metacognition in education (pp. 319339). Mahwah, NJ: Routledge.Google Scholar
Bannert, M. & Reimann, P. (2012). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 40, 193211.Google Scholar
Barab, S., Dodge, T., Tuzun, H., Job-Sluder, K. Jr., Gilbertson, R. C., , J., et al. (2007). The Quest Atlantis project: A socially-responsive play space for learning. In Shelton, B. E. & Wiley, D. (eds.), The design and use of simulation computer games in education (pp. 159186). Rotterdam: Sense Publishers.Google Scholar
Bargh, J. A. & Schul, Y.. (1980). On the cognitive benefits of teaching. Journal of Educational Psychology, 72, 593604.Google Scholar
Barral, O., Eugster, M. J. A., Ruotsalo, T., Spapé, M. M., Kosunen, I., Ravaja, N., Kaski, S., & Jacucci, G. (2015). Exploring peripheral physiology as a predictor of perceived relevance in information retrieval. In Brdiczka, O., Chau, P., Crenini, G., Pan, S., & Kristensson, P. O. (eds.), Proceedings of the 20th international conference on intelligent user interfaces, (pp. 389399). New York: ACM.CrossRefGoogle Scholar
Benedek, M. & Kaernbach, C. (2010), Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology, 47, 647658.Google ScholarPubMed
Biswas, G., Jeong, H., Kinnebrew, J. S., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology Enhanced Learning, 5, 123152.Google Scholar
Biswas, G., Segedy, J. R., & Bunchongchit, K. (2016). From design to implementation to practice a learning by teaching system: Betty’s Brain. International Journal of Artificial intelligence in Education, 26, 350364.Google Scholar
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417444.Google Scholar
Boucsein, W. (2012). Electrodermal activity. New York: Springer.Google Scholar
Bradbury, A.E., Taub, M., & Azevedo, R. 2017. The effects of autonomy on emotions and learning in game-based learning environments. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. J. (eds.), Proceedings of the 39th annual meeting of the Cognitive Science Society (pp. 16661671). Austin, TX: Cognitive Science Society.Google Scholar
Brawner, K. W. & Gonzalez, A. J. (2016). Modeling a learner’s affective state in real time to improve intelligent tutoring effectiveness. Theoretical Issues in Ergonomics, 17, 183210.Google Scholar
Burkett, C. & Azevedo, R. (2012). The effect of multimedia discrepancies on metacognitive judgments. Computers and Human Behavior, 28, 12761285.CrossRefGoogle Scholar
Clark, D. B., Tanner-Smith, E. E., & Killingsworth, S. S. (2016). Digital games, design, and learning: A systematic review and meta-analysis. Review of Educational Research, 86, 79122.CrossRefGoogle ScholarPubMed
Conati, C., Jaques, N., & Muir, M. (2013). Understanding attention to adaptive hints in educational games: An eye-tracking study. International Journal of Artificial Intelligence in Education, 23, 136161.Google Scholar
D’Mello, S. K. & Graesser, A. C. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145157.Google Scholar
D’Mello, S. K., Lehman, B. Pekrun, R., & Graesser, A. C. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153170.CrossRefGoogle Scholar
Duffy, M. & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338348.Google Scholar
Dunlosky, J. & Metcalfe, J. (2009). Metacognition: A textbook for cognitive, educational, life span and applied psychology. Newbury Park, CA: SAGE.Google Scholar
Greene, J. A. & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Education Psychology, 34, 1829.Google Scholar
Gross, J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26, 126.Google Scholar
Hacker, D. J., Dunlosky, J., & Graesser, A. C. (2009). Handbook of metacognition in education. New York: Routledge.Google Scholar
Hardy, M., Wiebe, E. N, Grafsgaard, J. F., Boyer, K. E., & Lester, J. C. (2013). Physiological responses to events during training: Use of skin conductance to inform future adaptive learning systems. In Proceedings of the human factors and ergonomic society 57th annual meeting (pp. 21012105).Google Scholar
Harley, J. M., Bouchet, F., Hussain, M. 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, 615625.Google Scholar
Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2018). “Let’s set up some subgoals”: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11, 5466.Google Scholar
Heilig, M. (1962). United States Patent No. #3,050,870. Alexandria, VA: United States Patent Office.Google Scholar
iMotions. (2017). Attention tool (Version 6.3) [Computer software]. Boston, MA: iMotions Inc.Google Scholar
Järvelä, S., Malmberg, J., & Koivuniemi, M. (2016). Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL. Learning and Instruction, 42, 111.Google Scholar
Kulik, J. A. & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86, 4278.Google Scholar
Lajoie, S. P., Poitras, E. G., Doleck, T., & Jarrell, A. (2015). Modeling metacognitive activities in medical problem-solving with BioWorld. In Peña-Ayala, A. (ed.), Metacognition: Fundamentals, applications, and trends. A profile of the current state-of the-art (pp. 323343). New York: Springer.Google Scholar
Lin, M., Preston, A., Kharrufa, A., & Kong, Z. (2016). Making L2 learners’ reasoning skills visible: The potential of computer supported collaborative learning environments. Thinking Skills and Creativity, 22, 303322.Google Scholar
Matsuda, N., Yarzebinski, E., Keiser, V., Raizada, R., Cohen, W. W., Stylianides, G. J., & Koedinger, K. R. (2013). Cognitive anatomy of tutor learning: Lessons learned with SimStudent. Journal of Educational Psychology, 105, 11521163.Google Scholar
Mayer, R. E. (2014a). Computer games for learning: An evidence-based approach. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Mayer, R. E. (ed.). (2014b). The Cambridge handbook of multimedia learning, 2nd edn. Cambridge, MA: Cambridge University Press.Google Scholar
Mayer, R. E. & Alexander, P. A. (eds.). (2017). Handbook of research on learning and instruction, 2nd edn. New York: Routledge.Google Scholar
Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers and Education, 70, 2940.Google Scholar
Mudrick, N. V., Rowe, J., Taub, M., Lester, J., & Azevedo, R. (2017). Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion. In Busso, C. & Epps, J. (eds.) Proceedings of the 2017 seventh international conference on affective computing and intelligent interaction (ACII) (pp. 184189). Washington, DC: IEEE Computer Society.Google Scholar
Nelson, T. O. & Narens, L. (1990). Metamemory: A theoretical framework and new findings. The Psychology of Learning and Motivation, 26, 125173.Google Scholar
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The achievement emotions questionnaire (AEQ). Contemporary Educational Psychology, 36, 3648.Google Scholar
Rayner, K. (2009). Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology, 62, 14571506.CrossRefGoogle ScholarPubMed
Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21, 115133.Google Scholar
Santos, M. E. C., Chen, A., Taketomi, T., Yamamoto, G., Miyazaki, J., & Kato, H. (2014). Augmented reality learning experiences: Survey of prototype design and evaluation. IEEE Transactions on Learning Technologies, 7, 3856.Google Scholar
Sawyer, R. K. (2014). The Cambridge handbook of the learning sciences New York: Cambridge University Press.Google Scholar
Sawyer, R., Smith, A., Rowe, J., Azevedo, R., Lester, J., & Carolina, N. (2017). Is more agency better? The impact of student agency on game-based learning. In André, E., Baker, R., Hu, X., Rodrigo, M., & du Bouley, B. (eds.) Proceedings of the 18th international conference on artificial intelligence in education (pp. 335346). Amsterdam: Springer.Google Scholar
Scheiter, K. & Eitel, A. (2016). The use of eye tracking as a research and instructional tool in multimedia learning. In Was, C. A., Sansosti, F. J., & Morris, B. (eds.), Eye-tracking technology applications in educational research (pp. 143164). Hershey, PA: IGI Global.Google Scholar
Schunk, D. & Greene, J. (2017). Handbook of self-regulation of learning and performance, 2nd edn. New York: Routledge.Google Scholar
Taub, M. & Azevedo, R. (2016a). Using multi-channel data to assess, understand, and support affect and metacognition with Intelligent Tutoring Systems. In Micarelli, A., Stamper, J., & Panourgia, K. (eds.), Proceedings of the 13th international conference on intelligent tutoring systems (pp. 543544). Amsterdam: Springer.Google Scholar
Taub, M. & Azevedo, R. (2016b). Using eye-tracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia- learning environment? In Micarelli, A., Stamper, J., & Panourgia, K. (eds.), Proceedings of the 13th international conference on intelligent tutoring systems (pp. 3447). Amsterdam: Springer.Google Scholar
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, 356–36.Google Scholar
Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., & Lester, J. (2018). Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learning and Instruction, 54, 93103.Google Scholar
Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2017). Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with Crystal Island. Computers in Human Behavior, 76, 641655.Google Scholar
Taub, M., Mudrick., N., Bradbury, A. E., & Azevedo, R. (in press). Self-regulation, selfexplanation, and reflection in game-based learning. In Plass, J., Horner, B., & Mayer, R. (eds.), Handbook of game-based learning. Boston, MA: MIT Press.Google Scholar
Trevors, G., Duffy, M., & Azevedo, R. (2014). Note-taking within MetaTutor: Interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educational Technology Research and Development, 62, 507528.CrossRefGoogle Scholar
Winne, P. H. (2018). Cognition and metacognition in self-regulated learning. In Schunk, D. & Greene, J. (eds.), Handbook of self-regulation of learning and performance (2nd edn) (pp. 3648). New York: Routledge.Google Scholar
Winne, P. H. & Azevedo, R. (2014). Metacognition. In Sawyer, K. (ed.), Cambridge handbook of the learning sciences, 2nd edn. (pp. 6387). Cambridge, MA: Cambridge University Press.Google Scholar
Winne, P., & Hadwin, A. (2008). The weave of motivation and self-regulated learning. In Schunk, D. & Zimmerman, B. (eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 297314). New York: Taylor & Francis.Google Scholar
Wolff, B. (2009). Building intelligent interactive tutors: Student-centered strategies for adaptive e-learning. Burlington, MA: Morgan Kaufmann.Google Scholar

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