Skip to main content
SpringerLink
Log in

Metacognition in Collaborative Learning

  • Chapter
  • First Online:
International Handbook of Computer-Supported Collaborative Learning

Part of the book series: Computer-Supported Collaborative Learning Series ((CULS,volume 19))

Abstract

Research has shown that metacognition plays a role in collaborative learning. We view metacognition as a central process supporting all modes of regulation (i.e., self-regulation, shared regulation, and co-regulation), as it enables learners to control and adapt their cognition, motivation, emotion, and behavior at both the individual and group levels. Our claim is that metacognitive monitoring and regulation of collaborative learning can help reduce the collaborative/transactive costs in collaboration and, therefore, contributes to success in computer-supported collaborative learning (CSCL). In this chapter, we discuss the role of metacognition in CSCL and broaden the discussion to regulation. Since regulation in CSCL has been studied increasingly, we review the current state of the art in that research and conclude how technological and digital tools could be implemented for studying and supporting metacognition and regulation in CSCL.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2017). Regulation and socio-emotional interactions in a positive and a negative group climate. Metacognition and Learning, 13, 57–90. https://doi.org/10.1007/s11409-017-9178-x.

    Article  Google Scholar 

  • Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175–1184.

    Article  Google Scholar 

  • Buder, J., Bodemer, D., & Ogata, H. (this volume). Group awareness. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning. Cham: Springer.

    Google Scholar 

  • Cress, U., Stahl, G., Rosé, C., Law, N., & Ludvigsen, S. (2018). Forming social systems by coupling minds at different levels of cognition: Design, tools, and research methods. International Journal of Computer-Supported Collaborative Learning, 13, 235–240. https://doi.org/10.1007/s11412-018-9284-z.

    Article  Google Scholar 

  • De Wever, B., & Strijbos, J.-W. (this volume). Roles for structuring groups for collaboration. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning. Cham: Springer.

    Google Scholar 

  • Dillenbourg, P. (1999). Introduction: What do you mean by “Collaborative learning”? In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 1–19). Amsterdam: Pergamon.

    Google Scholar 

  • Dinsmore, D. L., Alexander, P. A., & Loughlin, S. M. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20(4), 391–409.

    Article  Google Scholar 

  • Fischer, F., & Mandl, H. (2005). Knowledge convergence in computer-supported collaborative learning: The role of external representation tools. Journal of the Learning Sciences, 14(3), 405–441. https://doi.org/10.1207/s15327809jls1403_3.

    Article  Google Scholar 

  • Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911.

    Article  Google Scholar 

  • Fransen, J., Weinberger, A., & Kirschner, P. A. (2013). Team effectiveness and team development in CSCL. Educational Psychologist, 48(1), 9–24.

    Article  Google Scholar 

  • Haataja, E., Malmberg, J., & Järvelä, S. (2018). Monitoring in collaborative learning: Co-occurrence of observed behavior and physiological synchrony explored. Computers in Human Behavior, 87, 337–347. https://doi.org/10.1016/j.chb.2018.06.007.

    Article  Google Scholar 

  • Hadwin, A. F., Järvelä, S., & Miller, M. (2017). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 83–106). London: Routledge.

    Chapter  Google Scholar 

  • Iiskala, T., Volet, S. E., Lehtinen, E., & Vauras, M. (2015). Socially shared metacognitive regulation in asynchronous CSCL in science: Functions, evolution and participation. Frontline Learning Research, 3(1), 78–111. https://doi.org/10.14786/flr.v3i1.159.

    Article  Google Scholar 

  • Järvelä, S., & Hadwin, A. F. (2013). New Frontiers: Regulating Learning in CSCL. Educational Psychologist, 48(1), 25–39. https://doi.org/10.1080/00461520.2012.748006.

    Article  Google Scholar 

  • Järvelä, S., Järvenoja, H., Malmberg, J., & Hadwin, A. F. (2013). Part 1 : Underexplored Contexts and Populations in Self-Regulated Learning. Exploring Socially Shared Regulation. Journal of Cognitive Education and Psychology, 12(3), 267–286.

    Article  Google Scholar 

  • Järvelä, S., Kirschner, P. A., Järvenoja, H., Malmberg, J., Miller, M., & Laru, J. (2016). Socially shared regulation of learning in CSCL: Understanding and prompting individual- and group-level shared regulatory activities. International Journal of Computer-Supported Collaborative Learning, 11(3), 263–280. https://doi.org/10.1007/s11412-016-9238-2.

    Article  Google Scholar 

  • Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. (2019). What multimodal data can tell us about the self-regulated learning process? Learning and Instruction. https://doi.org/10.1016/j.learninstruc.2019.04.004.

  • 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, 1–11. https://doi.org/10.1016/j.learninstruc.2015.10.006.

    Article  Google Scholar 

  • Järvenoja, H., Järvelä, S., & Malmberg, J. (2017). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70, 101090. https://doi.org/10.1016/j.learninstruc.2017.11.004.

    Article  Google Scholar 

  • Jeong, H., & Chi, M. T. H. (2007). Knowledge convergence and collaborative learning. Instructional Science, 35(4), 287–315. https://doi.org/10.1007/s11251-006-9008-z.

    Article  Google Scholar 

  • Jeong, H., & Hartley, K. (2018). Theoretical and methodological frameworks for computer-supported collaborative learning. In F. Fischer, C. E. Hmelo-Silver, P. Reimann, & S. R. Goldman (Eds.), International handbook of the learning sciences (pp. 330–339). New York: Taylor & Francis.

    Chapter  Google Scholar 

  • Jeong, H., & Hmelo-Silver, C. E. (2016). Seven affordances of computer-supported collaborative learning: How to support collaborative learning? How can technologies help? Educational Psychologist, 51(2), 247–265. https://doi.org/10.1080/00461520.2016.1158654.

    Article  Google Scholar 

  • Kalyuga, S. (2013). Enhancing transfer by learning generalized domain knowledge structures. European Journal of Psychology of Education, 28(4), 1477–1493.

    Article  Google Scholar 

  • Kirschner, F., Paas, F., & Kirschner, P. A. (2009). A cognitive-load approach to collaborative learning: United brains for complex tasks. Educational Psychology Review, 21, 31–42.

    Article  Google Scholar 

  • Kirschner, F., Paas, F., Kirschner, P. A., & Janssen, J. (2011). Differential effects of problem-solving demands on individual and collaborative learning outcomes. Learning and Instruction, 21, 587–599.

    Article  Google Scholar 

  • Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, J. (2018). From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning, 13, 213–233.

    Article  Google Scholar 

  • Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353. https://doi.org/10.1016/S0747-5632(02)00057-2.

    Article  Google Scholar 

  • Kruger, A. C., & Tomasello, M. (1986). Transactive discussions with peers and adults. Developmental Psychology, 22, 681–685.

    Article  Google Scholar 

  • Lajoie, S. P., Lee, L., Poitras, E., Bassiri, M., Kazemitabar, M., Cruz-Panesso, Hmelo-Silver, C., Wisemand, J., Chane, L., & Lu, J. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions. Computers in Human Behavior, 52, 601–616. https://doi.org/10.1016/j.chb.2014.11.073.

    Article  Google Scholar 

  • Malmberg, J., Järvelä, S., Holappa, J., Haataja, E., Huang, X., & Siipo, A. (2018). Going beyond what is visible: What multichannel data can reveal about interaction in the context of collaborative learning? Computers in Human Behavior, 96, 235–245. https://doi.org/10.1016/j.chb.2018.06.030.

    Article  Google Scholar 

  • Malmberg, J., Järvelä, S., & Järvenoja, H. (2017). Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology, 49, 160–174. https://doi.org/10.1016/j.cedpsych.2017.01.009.

    Article  Google Scholar 

  • Malmberg, J., Järvelä, S., Järvenoja, H., & Panadero, E. (2015). Promoting socially shared regulation of learning in CSCL: Progress of socially shared regulation among high- and low-performing groups. Computers in Human Behavior, 52(0), 562–572. https://doi.org/10.1016/j.chb.2015.03.082.

    Article  Google Scholar 

  • Miller, M., & Hadwin, A. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 52, 573–588. https://doi.org/10.1016/j.chb.2015.01.050.

    Article  Google Scholar 

  • Molenaar, I., & Järvelä, S. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning, 9(2), 75–85. https://doi.org/10.1007/s11409-014-9114-2.

    Article  Google Scholar 

  • Näykki, P., Järvelä, S., Kirschner, P. A., & Järvenoja, H. (2014). Socio-emotional conflict in collaborative learning-A process-oriented case study in a higher education context. International Journal of Educational Research, 68, 1–14. https://doi.org/10.1016/j.ijer.2014.07.001.

    Article  Google Scholar 

  • Näykki, P., Järvenoja, H., Järvelä, S., & Kirschner, P. (2017). Monitoring makes a difference: Quality and temporal variation in teacher education students’ collaborative learning. Scandinavian Journal of Educational Research, 61(1), 31–46. https://doi.org/10.1080/00313831.2015.1066440.

    Article  Google Scholar 

  • Popov, V., van Leeuwen, A., & Buis, S. C. A. (2017). Are you with me or not? Temporal synchronicity and transactivity during CSCL. Journal of Computer Assisted Learning, 33, 424–442.

    Article  Google Scholar 

  • Prichard, J. S., & Ashleigh, M. J. (2007). The effects of team-skills training on transactive memory and performance. Small Group Research, 38(6), 696–726.

    Article  Google Scholar 

  • Rogat, T. K., & Adams-Wiggins, K. R. (2014). Other-regulation in collaborative groups: Implications for regulation quality. Instructional Science, 42(6), 879–904. https://doi.org/10.1007/s11251-014-9322-9.

    Article  Google Scholar 

  • Rogat, T. K., & Linnenbrink-Garcia, L. (2011). Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29(4), 375–415. https://doi.org/10.1080/07370008.2011.607930.

    Article  Google Scholar 

  • Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. Computer-Supported Collaborative Learning, 128, 69–97. https://doi.org/10.1145/130893.952914.

    Article  Google Scholar 

  • Rosé, C. (2018). Learning analytics in the learning sciences. In F. Fischer, C. E. Hmelo-Silver, P. Reimann, & S. R. Goldman (Eds.), International handbook of the learning sciences (pp. 511–519). New York: Taylor & Francis.

    Chapter  Google Scholar 

  • Schwartz, D. L. (1995). The emergence of abstract representations in dyad problem solving. The Journal of the Learning Sciences, 4(3), 321–354. https://doi.org/10.1207/s15327809jls0403_3.

    Article  Google Scholar 

  • Sobocinski, M., Malmberg, J., & Järvelä, S. (2017). Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions. Metacognition and Learning, 12, 275–294. https://doi.org/10.1007/s11409-016-9167-5.

    Article  Google Scholar 

  • Sonnenberg, C., & Bannert, M. (2015). Discovering the effects of metacognitive prompts on the sequential structure of srl-processes using process mining techniques. Journal of Learning Analytics, 2(1), 72–100. https://doi.org/10.18608/jla.2015.21.5.

    Article  Google Scholar 

  • Su, Y., Li, Y., Hu, H., & Rosé, C. P. (2018). Exploring college English language learners’ self and social regulation of learning during wiki-supported collaborative reading activities. International Journal of Computer-Supported Collaborative Learning, 13, 35–60. https://doi.org/10.1007/s11412-018-9269-y.

    Article  Google Scholar 

  • Vogel, F., Weinberger, A., & Fischer, F. (this volume). Collaboration scripts: Guiding, internalizing, and adapting. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning. Cham: Springer.

    Google Scholar 

  • Volet, S., Summers, M., & Thurman, J. (2009). High-level co-regulation in collaborative learning: How does it emerge and how is it sustained? Learning and Instruction, 19(2), 128–143.

    Article  Google Scholar 

  • Volet, S., Vauras, M., Salo, A. E., & Khosa, D. (2017). Individual contributions in student-led collaborative learning: Insights from two analytical approaches to explain the quality of group outcome. Learning and Individual Differences, 53, 79–92. https://doi.org/10.1016/j.lindif.2016.11.006.

    Article  Google Scholar 

  • Wang, X., Kollar, I., & Stegmann, K. (2017). Adaptable scripting to foster regulation processes and skills in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 12(2), 153–172. https://doi.org/10.1007/s11412-017-9254-x.

    Article  Google Scholar 

  • Winne, P. H. (2018). Cognition and metacognition in self-regulated learning. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 36–48). New York, NY: Routledge.

    Google Scholar 

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). London: Routledge.

    Google Scholar 

  • Wolters, C., & Won, S. (2017). Validity and the use of self-report questionnaires to assess self-regulated learning. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 307–322). New York: Routledge.

    Chapter  Google Scholar 

  • Zambrano, J. R., Kirschner, F., & Kirschner, P. (2019). How cognitive load theory can be applied to collaborative learning: Collaborative cognitive load theory. In S. Tindall-Ford, S. Agostinho, & J. Sweller (Eds.), Advances in cognitive load theory: Rethinking teaching (pp. 30–40). London: Routledge. https://doi.org/10.4324/9780429283895-3.

    Chapter  Google Scholar 

  • Zhang, J., Scardamalia, M., Reeve, R., & Messina, R. (2009). Designs for collective cognitive responsibility in knowledge-building communities. Journal of the Learning Sciences, 18(1), 7–44. https://doi.org/10.1080/10508400802581676.

    Article  Google Scholar 

Further Readings

  • Haataja, E., Malmberg, J., & Järvelä, S. (2018). Monitoring in collaborative learning: Co-occurrence of observed behavior and physiological synchrony explored. Computers in Human Behavior, 87, 337–347. https://doi.org/10.1016/j.chb.2018.06.007. This empirical study is one of the first studies monitoring in collaborative learning by videos and physiological data. It studied how students in a group monitor their cognitive, affective, and behavioral processes during their collaboration, as well as how observed monitoring co-occurs with their physiological synchrony during the collaborative learning session. The results indicate not only the role metacognitive monitoring plays in regulation of the collaborative learning process but also that physiological synchrony could potentially shine a light on the joint regulation processes of collaborative learning groups.

    Article  Google Scholar 

  • Järvelä, S., Hadwin, A. F., Malmberg, J., & Miller, M. (2018). Contemporary perspectives of regulated learning in collaboration. In F. Fischer, C. E. Hmelo-Silver, P. Reimann, & S. R. Goldman (Eds.), Handbook of the Learning Sciences (pp. 127–136). New York: Taylor & Francis. Grounding on decades of self-regulated learning research, this chapter shows that in order to succeed in solo and collaborative learning tasks, students need to develop regulating learning skills and strategies on their own, with peers, and in groups. It introduces the three forms of regulation (self-regulation, co-regulation, and shared regulation), explains the critical processes in regulation, and elaborates what is and what is not regulated learning. The chapter also has recommendations to the design principles and technologies to support regulated learning.

    Chapter  Google Scholar 

  • Järvelä, S., Kirschner, P. A., Hadwin, A., Järvenoja, H., Malmberg, J., Miller, M., & Laru, J. (2016). Socially shared regulation of learning in CSCL: Understanding and prompting individual- and group-level shared regulatory activities. International Journal of Computer Supported Collaborative Learning, 11(3), 263–280. https://doi.org/10.1007/s11412-016-9238-2. This paper argues that few studies examine the effectiveness and efficiency of CSCL with respect to cognitive, motivational, emotional, and social issues, despite the fact that the role of regulatory processes is critical for the quality of students’ engagement in collaborative learning settings. The authors review the four earlier lines in developing support in CSCL and show how there has been a lack of work to support individuals in groups to engage in, sustain, and productively regulate their own and the group’s collaborative processes. It is discussed how socially shared regulation of learning (SSRL) contributes to effective and efficient CSCL, what tools are presently available, and what the implications of research on these tools are for future tool development.

    Article  Google Scholar 

  • Jeong, H., & Hmelo-Silver, C. E. (2016). Seven affordances of computer-supported collaborative learning: How to support collaborative learning? How can technologies help? Educational Psychologist, 51(2), 247–265. https://doi.org/10.1080/00461520.2016.1158654. This paper discusses about the critical processes in collaboration for CSCL. It introduces seven core affordances of technology for collaborative learning based on theories of collaborative learning and CSCL practices. Technology affords learner opportunities to (1) engage in a joint task, (2) communicate, (3) share resources, (4) engage in productive collaborative learning processes, (5) engage in co-construction, (6) monitor and regulate collaborative learning, and (7) find and build groups and communities. The proposed framework is illustrated using in-depth explorations of how technologies are actually used to support collaborative learning in CSCL research and identify representative design strategies and technology examples.

    Article  Google Scholar 

  • Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, J. (2018). From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning, 13, 213–233. This paper discusses how cognitive load theory can be associated not only individual learning but also efficiency and effectiveness of (computer-supported) collaborative learning. While the theory has been considered in instructional design in individual learning, it has often not considered when designing collaborative learning situations or CSCL. This paper illustrates how and why cognitive load theory can throw light on collaborative learning and generate principles specific to the design and study of collaborative learning.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Educational Sciences and Teacher Education, University of Oulu, Oulu, Finland

    Sanna Järvelä, Jonna Malmberg, Marta Sobocinski & Paul A. Kirschner

  2. Open University of the Netherlands, Heerlen, The Netherlands

    Paul A. Kirschner

Authors
  1. Sanna Järvelä
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonna Malmberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marta Sobocinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul A. Kirschner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanna Järvelä .

Editor information

Editors and Affiliations

  1. Leibniz-Institut für Wissensmedien, Tübingen, Germany

    Ulrike Cress

  2. Language Technologies Institute and Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Rosé

  3. Department of Administration Leadership, and Technology, New York University, New York, NY, USA

    Alyssa Friend Wise

  4. Faculty of Informatics, Shizuoka University, Hamamatsu-shi, Shizuoka, Japan

    Jun Oshima

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Järvelä, S., Malmberg, J., Sobocinski, M., Kirschner, P.A. (2021). Metacognition in Collaborative Learning. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds) International Handbook of Computer-Supported Collaborative Learning. Computer-Supported Collaborative Learning Series, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-65291-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65291-3_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65290-6

  • Online ISBN: 978-3-030-65291-3

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation