Learners’ regulatory actions as interleaving between moments of productive knowledge sharing during online collaboration
Main Article Content
Abstract
Collaborative problem solving in digital environments requires student groups to not only co-construct their ideas as a group but also regulate several aspects of their teamwork. Although prior research has linked group regulation to the group’s task performance, less is known about the immediate effects of regulation. Specifically, limited research has examined the types of regulation that could promote productive knowledge sharing during ongoing interactions. This study explored how six groups of varying performance levels exercised regulatory actions between moments of productive knowledge sharing (PKS) during text-based online interactions. We employed a computational technique to analyze the temporal changes in groups’ idea networks, identifying episodes where groups’ idea exchanges reached a productive level. Interpretations of students’ regulatory actions between PKS episodes were facilitated by Epistemic Network Analysis (ENA). Findings highlight the importance of examining multiple facets of regulatory actions at both the group and individual levels while contextualizing these actions within meaningful knowledge-sharing episodes. Socioemotional regulation was notably lacking during pre-task planning in low-performing groups. The lack of shared planning to reaffirm the group’s goal before starting the task appears to relate to groups experiencing behavioural coordination challenges, which likely reduce the chances of achieving PKS. Following instances of PKS, high-performing groups often monitored and evaluated their ideas or cognition, whereas low-performing groups focused on monitoring how the task was being completed. Patterns of individual members’ regulation across the ENA space revealed that members of high-performing groups regulated actively by diversifying their actions. The implications for supporting collaboration are discussed.
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References
Bakhtiar, A., & Hadwin, A. F. (2020). Dynamic interplay between modes of regulation during motivationally challenging episodes in collaboration. Frontline Learning Research, 8(2), 1-34. https://doi.org/10.14786/flr.v8i2.561
Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2018). 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
Barany, A., Philips, M., Kawakubo, A. J. T., and Oshima, J. (2022). Choosing units of analysis in temporal discourse. In B. Wasson, & S. Zörgő (Eds.,), Advances in quantitative ethnography (pp. 80-94). Springer.
Bowman, D., Swiecki, Z., Cai, Z., Wang, Y., Eagan, B., Linderoth, J., & Shaffer, D. W. (2021). The mathematical foundations of epistemic network analysis. In A. R. Ruis, & S. B. Lee (Eds.), Advances in quantitative ethnography (pp. 91-105). Springer.
Damşa, C. I., Kirschner, P. A., Andriessen, J. E. B., Erkens, G., & Sins, P. H. M. (2010). Shared epistemic agency: An empirical study of an emergent construct. Journal of the Learning Sciences, 19(2), 143-186. https://doi.org/10.1080/10508401003708381
Elmoazen, R., Saqr, M., Tedre, M., & Hirsto, L. (2022). A systematic literature review of empirical research on epistemic network analysis in education. IEEE Access, 10, 17330-17348.
Feng, X., van Aalst, J., Chan, C. K., & Yang, Y. (2019). Fostering collective knowledge advancement through Idea-Friend Maps in a large knowledge building community. In A wide lens: Combining embodied, enactive, extended, and embedded learning in collaborative settings (pp. 288-295). ISLS.
Gao, Y., Wu, L., Lv, X., Ma, X., & Liu, Q. (2024). A joint evaluation method of regulated‐learning and cognitive quality in collaborative knowledge building. Journal of Computer Assisted Learning, 40(4), 1806-1822. https://doi.org/10.1111/jcal.12985
Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4), 397-431. https://doi.org/10.2190/7MQV-X9UJ-C7Q3-NRAG
Gyasi, J. F., & Zheng, L. (2023). Idea improvement and socially shared regulation matter in cross-cultural online collaborative learning. SAGE Open, 13(1), 1-17. https://doi.org/10.1177/21582440221148625
Haataja, E., Dindar, M., Malmberg, J., & Järvelä, S. (2022). Individuals in a group: Metacognitive and regulatory predictors of learning achievement in collaborative learning. Learning and Individual Differences, 96, 102146. https://doi.org/10.1016/j.lindif.2022.102146.
Hadwin, A. F., Bakhtiar, A., & Miller, M. (2018). Challenges in online collaboration: Effects of scripting shared task perceptions. International Journal of Computer-Supported Collaborative Learning, 13, 301-329. https://doi.org/10.1007/s11412-018-9279-9
Hadwin, A., Järvelä, S., & Miller, M. (2018). 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 (pp. 83-106). Routledge.
Ito, T., Umemoto, T., & Nakaya, M. (2024). Reciprocal path model of autonomous motivation and motivational regulation: socially shared regulation in intellectual group activities. Journal of Educational and Developmental Psychology, 14(1), 93-107. https://doi.org/10.5539/jedp.v14n1p93
Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition and Learning, 7, 25-43. https://doi.org/10.1007/s11409-010-9061-5
Järvelä, S., & Hadwin, A. (2024). Triggers for self-regulated learning: A conceptual framework for advancing multimodal research about SRL. Learning and Individual Differences, 115, 102526. https://doi.org/10.1016/j.lindif.2024.102526
Järvelä, S., & Rose, C. P. (2022). Novel CSCL design processes prompted by exploration of role-playing and emotions. International Journal of Computer-Supported Collaborative Learning, 17(4), 457-461. https://doi.org/10.1007/s11412-022-09383-9
Järvenoja, H., Malmberg, J., Törmänen, T., Mänty, K., Haataja, E., Ahola, S., & Järvelä, S. (2020). A collaborative learning design for promoting and analyzing adaptive motivation and emotion regulation in the science classroom. Frontiers in Education, 5(111), 1-16. https://doi.org/10.3389/feduc.2020.00111
Järvenoja, H., Näykki, P., & Törmänen, T. (2019). Emotional regulation in collaborative learning: when do higher education students activate group level regulation in the face of challenges? Studies in Higher Education, 44(10), 1747-1757. https://doi.org/10.1080/03075079.2019.1665318
Kaliisa, R., Dane, J. O., Sanchez, D., Pratt, J., Damsa, C., & Scianna, J. (2023). Understanding group dynamics during synchronous collaborative problem-solving activities: An epistemic network Approach. In G. Arastoopour Irgens, & S. Knight (Eds.), Advances in quantitative ethnography (pp. 81-95). Springer.
Lai, C. L. (2021). Effects of the group‐regulation promotion approach on students’ individual and collaborative learning performance, perceptions of regulation and regulation behaviours in project‐based tasks. British Journal of Educational Technology, 52(6), 2278-2298. https://doi.org/10.1111/bjet.13138
Lee, L., Lajoie, S. P., Poitras, E. G., Nkangu, M., & Doleck, T. (2017). Co-regulation and knowledge construction in an online synchronous problem based learning setting. Education and Information Technologies, 22(4), 1623-1650. https://doi.org/10.1007/s10639-016-9509-6
Lehtinen, A., Kostiainen, E., & Näykki, P. (2023). Co-construction of knowledge and socioemotional interaction in pre-service teachers’ video-based online collaborative learning. Teaching and Teacher Education, 133, 104299. https://doi.org/10.1016/j.tate.2023.104299
Lobczowski, N. G., Lyons, K., Greene, J. A., & McLaughlin, J. E. (2021). Socially shared metacognition in a project-based learning environment: A comparative case study. Learning, Culture and Social Interaction, 30, 100543. https://doi.org/10.1016/j.lcsi.2021.100543
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, 562-572. https://doi.org/10.1016/j.chb.2015.03.082
Melzner, N., Greisel, M., Dresel, M., & Kollar, I. (2019). Using process mining (PM) and epistemic network analysis (ENA) for comparing processes of collaborative problem regulation. In B. Eagan, M. Misfeldt, & A. Siebert-Evenstone (Eds.), Advances in quantitative ethnography (pp. 154-164). Springer.
Nguyen, H., Lim, K. Y., Wu, L. L., Fischer, C., & Warschauer, M. (2021). “We’re looking good”: Social exchange and regulation temporality in collaborative design. Learning and Instruction, 74, 101443. https://doi.org/10.1016/j.learninstruc.2021.101443
Ohsaki, A., & Oshima, J. (2021). Socio-semantic network analysis of knowledge-creation discourse on a real-time scale. In A. R. Ruis & S. B. Lee (Eds.), Advances in quantitative ethnography (pp. 170–184). Springer.
Oshima, J., Oshima, R., & Matsuzawa, Y. (2012). Knowledge building discourse explorer: A social network analysis application for knowledge building discourse. Educational Technology Research and Development, 60(5), 903-921. https://doi.org/10.1007/s11423-012-9265-2
Oshima, J., Oshima, R., & Saruwatari, S. (2020). Analysis of students’ ideas and conceptual artifacts in knowledge‐building discourse. British Journal of Educational Technology, 51(4), 1308-1321. https://doi.org/10.1111/bjet.12961
Ouyang, F., Wu, M., Zhang, L., Xu, W., Zheng, L., & Cukurova, M. (2023). Making strides towards AI-supported regulation of learning in collaborative knowledge construction. Computers in Human Behavior, 142, 107650. https://doi.org/10.1016/j.chb.2023.107650
Panadero, E., Kirschner, P. A., Järvelä, S., Malmberg, J., & Järvenoja, H. (2015). How individual self-regulation affects group regulation and performance: A shared regulation intervention. Small Group Research, 46(4), 431–454. https://doi.org/10.1177/104649641559121
Paquette, L., Grant, T., Zhang, Y., Biswas, G., & Baker, R. (2021). Using epistemic networks to analyze self-regulated learning in an open-ended problem-solving environment. In A. R. Ruis, & S. B. Lee (Eds.), Advances in quantitative ethnography (pp. 185–201). Springer.
Rogat, T. K., & Adams-Wiggins, K. R. (2014). Other-regulation in collaborative groups: Implications for regulation quality. Instructional Science, 42, 879-904. https://doi.org/10.1007/s11251-014-9322-9
Saint, J., Dragan, G., Wannisa, M., Ahmad Uzir, N.A, & Pardo, A. (2020). Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 402–11). ACM.
Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 97-118). Cambridge University Press.
Shaffer, D. W. (2006). Epistemic frames for epistemic games. Computers & Education, 46(3), 223-234. https://doi.org/10.1016/j.compedu.2005.11.003
Shaffer, D., & Ruis, A. (2017). Epistemic network analysis: A worked example of theory-based learning analytics. In C. Lang, G. Siemens, A. F. Wise, & D. Gasevic (Eds.), Handbook of Learning Analytics (pp. 175–187). SOLAR.
Siebert-Evenstone, A. L., Irgens, G. A., Collier, W., Swiecki, Z., Ruis, A. R., & Shaffer, D. W. (2017). In search of conversational grain size: Modeling semantic structure using moving stanza windows. Journal of Learning Analytics, 4(3), 123-139. https://doi.org/10.18608/jla.2017.43.7
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(1), 35-60. https://doi.org/10.1007/s11412-018-9269-y
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. https://doi.org/10.1016/j.learninstruc.2008.03.001
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277-304). Lawrence Erlbaum.
Yang, S., Lodge, J.M. & Brooks, C. (2024). Capturing multi-layered regulated learning in collaboration. Metacognition Learning, 20(2), 1-39. https://doi.org/10.1007/s11409-024-09409-7
Yang, Y., Chen, Y., Feng, X., Sun, D., & Pang, S. (2024). Investigating the mechanisms of analytics-supported reflective assessment for fostering collective knowledge. Journal of Computing in Higher Education, 36(1), 242-273. https://doi.org/10.1007/s12528-024-09398-1
Yin, R. K. (2017). Case study research and applications: Design and methods (6th ed.). SAGE Publications.
Zabolotna, K., Malmberg, J., & Järvenoja, H. (2023). Examining the interplay of knowledge construction and group-level regulation in a computer-supported collaborative learning physics task. Computers in Human Behavior, 138, 107494. https://doi.org/10.1016/j.chb.2022.107494
Zabolotna, K., Nøhr, L., Iwata, M. Spikol, D., Malmberg, J., & Järvenoja, H. (2025). How does collaborative task design shape collaborative knowledge construction and group-level regulation of learning? A study of secondary school students’ interactions in two varied tasks. International Journal Computer-Supported Collaborative Learning, 20, 171–199 https://doi.org/10.1007/s11412-024-09442-3
Zhang, N., & Ouyang, F. (2023). An integrated approach for knowledge extraction and analysis in collaborative knowledge construction. International Journal of Educational Technology in Higher Education, 20(1), 45. https://doi.org/10.1186/s41239-023-00414-5
Zhang, S., Chen, J., Wen, Y., Chen, H., Gao, Q., & Wang, Q. (2021). Capturing regulatory patterns in online collaborative learning: A network analytic approach. International Journal of Computer-Supported Collaborative Learning, 16(1), 37-66. https://doi.org/10.1007/s11412-021-09339-5
Zheng, L., Li, X., Zhang, X., & Sun, W. (2019). The effects of group metacognitive scaffolding on group metacognitive behaviors, group performance, and cognitive load in computer-supported collaborative learning. The Internet and Higher Education, 42, 13–24. https://doi.org/10.1016/j.iheduc.2019.03.002
Zörgő, S. (2023). Segmentation and code co-occurrence accumulation: operationalizing relational context with stanza windows. In C. Damşa, & A. Barany (Eds.), Advances in quantitative ethnography (pp. 146-162). Springer.