Skip to main content
SpringerLink
Log in

Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions

  • Published:
Metacognition and Learning Aims and scope Submit manuscript

Abstract

Investigating the temporal order of regulatory processes can explain in more detail the mechanisms behind success or lack of success during collaborative learning. The aim of this study is to explore the differences between high- and low-challenge collaborative learning sessions. This is achieved through examining how the three phases of self-regulated learning occur in a collaborative setting and the types of interaction associated with these phases. The participants were teacher training students (N = 44), who worked in groups on a complex task related to didactics of mathematics during 6 weeks. The participants were instructed to use an application that was designed to increase awareness of the cognitive, motivational and emotional challenges the group might face. Based on the application’s log files, the sessions were categorized into low- and high-challenge sessions. The video data from each session were coded based on the self-regulation phases and the types of interaction. The frequencies of the phases and the types of interaction were calculated for each session, and process discovery methods were applied using the heuristic miner algorithm. The results show no significant differences between the sessions in the frequency of phases. However, the process models of the two sessions were different: in the high-challenge sessions, the groups switched between the forethought and performance phases more. In conclusion, the regulation phases and types of interaction that contribute to successful collaboration differ in high- and low challenge sessions and support for regulated learning is needed especially at the middle of the learning process.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Azevedo, R., & Witherspoon, A. (2009). Self-regulated learning with hypermedia. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 319–339). New York: Routledge.

    Google Scholar 

  • Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., & Fike, A. (2009). MetaTutor: A MetaCognitive tool for enhancing self-regulated learning. In R. Pirrone, R. Azevedo, & G. Biswas (Eds.), Proceedings of the AAAI Fall Symposium on Cognitive and Metacognitive Educational Systems (pp. 14–19). Menlo Park: AAAI Press.

    Google Scholar 

  • Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185. doi:10.1007/s11409-013-9107-6.

    Article  Google Scholar 

  • Bannert, M., Sonnenberg, C., Mengelkamp, C., & Pieger, E. (2015). Short- and long-term effects of students’ self-directed metacognitive prompts on navigation behavior and learning performance. Computers in Human Behavior, 52, 293–306. doi:10.1016/j.chb.2015.05.038.

    Article  Google Scholar 

  • Butler, D. L., & Cartier, S. C. (2004). Promoting effective task interpretation as an important work habit: A key to successful teaching and learning. Teachers College Record, 106(9), 1729–1758. doi:10.1111/j.1467-9620.2004.00403.x.

    Article  Google Scholar 

  • Cleary, T. J., & Zimmerman, B. J. (2012). A cyclical self-regulatory account of student engagement: Theoretical foundations and applications. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 237–257). Boston: Springer. doi:10.1007/978-1-4614-2018-7.

    Chapter  Google Scholar 

  • Cleary, T. J., Dong, T., & Artino, A. R. (2014). Examining shifts in medical students’ microanalytic motivation beliefs and regulatory processes during a diagnostic reasoning task. Advances in Health Sciences Education. doi:10.1007/s10459-014-9549-x.

    Google Scholar 

  • De Weerdt, J., De Backer, M., Vanthienen, J., & Baesens, B. (2012). A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems, 37(7), 654–676. doi:10.1016/j.is.2012.02.004.

    Article  Google Scholar 

  • Dillenbourg, P., Baker, M. J., Blaye, A., & O’Malley, C. (1995). The evolution of research on collaborative learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machine: Towards an interdisciplinary learning science (pp. 189–211). Oxford: Elsevier.

    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 Educational Psychology, 34(1), 18–29. doi:10.1016/j.cedpsych.2008.05.006.

    Article  Google Scholar 

  • Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 65–84). New York: Routledge.

    Google Scholar 

  • Järvelä, S., & Hadwin, A. F. (2013). New frontiers: regulating learning in CSCL. Educational Psychologist, 48(1), 25–39. doi: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., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., Koivuniemi, M., & Järvenoja, H. (2014). Enhancing socially shared regulation in collaborative learning groups: designing for CSCL regulation tools. Educational Technology Research and Development, 63(1), 125–142. doi:10.1007/s11423-014-9358-1.

    Article  Google Scholar 

  • Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., & Sobocinski, M. (2016a). How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement? Learning and Instruction, 1–13. doi:10.1016/j.learninstruc.2016.01.005.

  • Järvelä, S., Kirschner, P. A., Järvenoja, H., Malmberg, J., Miller, M., & Laru, J. (2016b). Socially shared regulation of learning in CSCL: Understanding and prompting individual- and group-level shared regulatory activities. International Journal of Computer-Supported Collaborative Learning.

  • Järvelä, S., Malmberg, J., & Koivuniemi, M. (2016c). Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL. Learning and Instruction, 42, 1–11. doi:10.1016/j.learninstruc.2015.10.006.

  • Järvenoja, H., & Järvelä, S. (2009). Emotion control in collaborative learning situations: do students regulate emotions evoked by social challenges? British Journal of Educational Psychology, 79(3), 463–481. doi:10.1348/000709909X402811.

    Article  Google Scholar 

  • Johnson, A. M., Azevedo, R., & D’Mello, S. K. (2011). The temporal and dynamic nature of self-regulatory processes during independent and externally assisted hypermedia learning. Cognition and Instruction, 29(4), 471–504. doi:10.1080/07370008.2011.610244.

    Article  Google Scholar 

  • Kapur, M. (2011). Temporality matters: advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment. International Journal of Computer-Supported Collaborative Learning, 6(1), 39–56. doi:10.1007/s11412-011-9109-9.

    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. doi:10.1016/S0747-5632(02)00057-2.

    Article  Google Scholar 

  • Kreijns, K., Kirschner, P. A., & Vermeulen, M. (2013). Social aspects of CSCL environments: a research framework. Educational Psychologist, 48(4), 229–242. doi:10.1080/00461520.2012.750225.

    Article  Google Scholar 

  • Kuvalja, M., Verma, M., & Whitebread, D. (2014). Patterns of co-occurring non-verbal behaviour and self-directed speech; a comparison of three methodological approaches. Metacognition and Learning, 9(2), 87–111. doi:10.1007/s11409-013-9106-7.

    Article  Google Scholar 

  • Malmberg, J., Järvenoja, H., & Järvelä, S. (2010). Tracing elementary school students’ study tactic use in gStudy by examining a strategic and self-regulated learning. Computers in Human Behavior, 26(5), 1034–1042. doi:10.1016/j.chb.2010.03.004.

    Article  Google Scholar 

  • Malmberg, J., Järvenoja, H., & Järvelä, S. (2013). Patterns in elementary school students’ strategic actions in varying learning situations. Instructional Science, 41(5), 933–954. doi:10.1007/s11251-012-9262-1.

    Article  Google Scholar 

  • Malmberg, J., Järvelä, S. & Kirschner, P. A. (2014). Elementary school students’ strategic learning: does task-type matter? Metacognition and Learning, 9(2), 113–136. doi:10.1007/s11409-013-9108-5.

  • Malmberg, J., Järvelä, S., Järvenoja, H., & Panadero, E. (2015a). Promoting socially shared regulation of learning in CSCL: progress of socially shared regulation among high- and low-performing groups. Computers in Human Behavior, 52, 1–18. doi:10.1016/j.chb.2015.03.082.

    Article  Google Scholar 

  • Malmberg, J., Järvenoja, H., Järvelä, S., & Panadero, E. (2015b). Socially-shared regulation of learning in computer supported collaborative learning. Computers in Human Behavior, 52, 562–572.

    Article  Google Scholar 

  • McCardle, L., & Hadwin, A. F. (2015). Using multiple, contextualized data sources to measure learners’ perceptions of their self-regulated learning. Metacognition and Learning, 10(1), 43–75. doi:10.1007/s11409-014-9132-0.

    Article  Google Scholar 

  • Molenaar, I., & Chiu, M. M. (2014). Dissecting sequences of regulation and cognition: statistical discourse analysis of primary school children’s collaborative learning. Metacognition and Learning, 9(2), 137–160. doi:10.1007/s11409-013-9105-8.

    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. doi:10.1007/s11409-014-9114-2.

    Article  Google Scholar 

  • Phielix, C., Prins, F. J., Kirschner, P. A., Erkens, G., & Jaspers, J. (2011). Group awareness of social and cognitive performance in a CSCL environment: effects of a peer feedback and reflection tool. Computers in Human Behavior, 27(3), 1087–1102. doi:10.1016/j.chb.2010.06.024.

    Article  Google Scholar 

  • Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego: Academic. doi:10.1016/B978-012109890-2/50043-3.

    Chapter  Google Scholar 

  • Reimann, P., Frerejean, J., & Thompson, K. (2009). Using process mining to identify models of group decision making in chat data. In: Proceedings of the 9th International Conference on Computer Supported Collaborative Learning. Vol. 1 (pp. 98–107). International Society of the Learning Sciences.

  • 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. doi:10.1080/07370008.2011.607930.

    Article  Google Scholar 

  • Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In: Computer-supported collaborative learning (pp. 69–97). Berlin, Germany: Springer. doi:10.1145/130893.952914.

  • Schoor, C., & Bannert, M. (2012). Exploring regulatory processes during a computer-supported collaborative learning task using process mining. Computers in Human Behavior, 28(4), 1321–1331. doi:10.1016/j.chb.2012.02.016.

    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.

    Article  Google Scholar 

  • van der Aalst, W. M. P. (2011). Process mining: Discovery, conformance and enhancement of business processes. Berlin: Springer. doi:10.1007/978-3-642-19345-3.

    Book  Google Scholar 

  • Weijters, A. J. M. M., Van Der Aalst, W. M. P., & AlvesdeMedeiros, A. K. (2006). Process mining with the heuristics miner algorithm. CIRP Annals-Manufacturing Technology, 166, 1–34.

  • Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276. doi:10.1080/00461520.2010.517150.

    Article  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). Mahwah: Erlbaum.

    Google Scholar 

  • Wolters, C. A., Benzon, M. B., & Arroy-Giner, C. (2011). Assessing strategies for the self-regulation of motivation. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 298–312). New York: Routledge.

    Google Scholar 

  • Yang, X., Li, J., Guo, X., & Li, X. (2015). Group interactive network and behavioral patterns in online English-to-Chinese cooperative translation activity. The Internet and Higher Education, 25, 28–36. doi:10.1016/j.iheduc.2014.12.003.

    Article  Google Scholar 

  • Zheng, L., & Yu, J. (2016). Exploring the behavioral patterns of co-regulation in mobile computer-supported collaborative learning. Smart Learning Environments, 3(1), 1. doi:10.1186/s40561-016-0024-4.

    Article  Google Scholar 

  • Zhou, M. (2013). Using traces to investigate self-regulatory activities: a study of self-regulation and achievement goal profiles in the context of web search for academic tasks. Journal of Cognitive Education and Psychology, 12(3), 287–306.

    Article  Google Scholar 

  • Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, 329–339. doi:10.1037/0022-0663.81.3.329.

    Article  Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 1–35). San Diego: Academic. doi:10.1016/B978-012109890-2/50031-7.

    Google Scholar 

  • Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 1–37). Mahwah: Erlbaum.

    Google Scholar 

  • Zimmerman, B. J. (2008). Investigating self-regulation and motivation: historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166–183. doi:10.3102/0002831207312909.

    Article  Google Scholar 

  • Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64). New York: Routledge. doi:10.4324/9780203839010.ch4.

    Google Scholar 

  • Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: a social cognitive career path. Educational Psychologist, 48(3), 135–147. doi:10.1080/00461520.2013.794676.

    Article  Google Scholar 

Download references

Acknowledgements

Research funded by the Finnish Academy, Project no. 259214 (PROSPECTS, PI: Sanna Järvelä).

Author information

Authors and Affiliations

  1. Learning and Educational Technology Research Unit, University of Oulu, P.O.BOX 2000, Oulu, FIN-90014, Finland

    Márta Sobocinski, Jonna Malmberg & Sanna Järvelä

Authors
  1. Márta Sobocinski
    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. Sanna Järvelä
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Márta Sobocinski.

Ethics declarations

Conflict of interest

The authors (Márta Sobocinski, Jonna Malmberg, Sanna Järvelä) declare that there is no conflict of interest.

Appendix

Appendix

Data examples of coded types of interaction. Students’ names have been changed. All examples are from one group’s term plan sessions.

figure afigure a

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11409-016-9167-5

Keywords

Search

Navigation