Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions
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.
KeywordsSelf-regulated learning Temporal patterns Process mining Video data Collaborative learning Interaction types
Research funded by the Finnish Academy, Project no. 259214 (PROSPECTS, PI: Sanna Järvelä).
Compliance with ethical standards
Conflict of interest
The authors (Márta Sobocinski, Jonna Malmberg, Sanna Järvelä) declare that there is no conflict of interest.
- 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
- 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.CrossRefGoogle 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
- 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., 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.CrossRefGoogle 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. Google Scholar
- 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.
- 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.
- 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.CrossRefGoogle 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.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.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.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
- 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