Skip to main content
Log in

Variations in socially shared metacognitive regulation and their relation with university students’ performance

  • Published:
Metacognition and Learning Aims and scope Submit manuscript

Abstract

The present study aims at investigating whether events of socially shared metacognitive regulation (SSMR) differ from each other when comparing their characteristics. These differences are labelled “variations in SSMR”. The study is conducted in a peer tutoring setting at university and includes video data (70 h of video recordings) on the regulation behaviour of thirty students who participated in a semester-long peer tutoring intervention that was directed at knowledge co-construction. In addition to studying variations in SSMR, the current study aims at examining whether individual students’ engagement in variations in SSMR is related to their performance on a knowledge test taken immediately after the peer tutoring intervention. Latent class cluster models were run to explore the presence of variations in SSMR. The trigger for SSMR, the number of students actively involved in SSMR, the level of elaboration during SSMR, and the function of SSMR in the collaborative learning process were included in the model as input parameters. A four-cluster model was selected as the best fitting model that demonstrated statistical significance. The four identified variations of SSMR were labelled as ‘interrogative SSMR’, ‘affirmative SSMR’, ‘interfering SSMR’, and ‘progressive SSMR’. Regression analyses revealed that not all variations in SSMR are equally important for predicting students’ performance. Students’ engagement in interrogative SSMR was significantly positively related to students’ performance on the knowledge test, whereas their engagement in interfering SSMR was negatively related. In contrast, the frequency of students’ involvement in affirmative SSMR or progressive SSMR demonstrated no significant relation with students’ performance. By unravelling the multifaceted character of SSMR, the present study allows to extend and to refine the emerging theory on shared regulation.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Volet et al. (2009) adopt the term ‘coregulation’ to refer to multiple students’ monitoring and regulation of joint activity at the group level. In line with Hadwin et al. (2017), the current study conceptualizes this behaviour as ‘socially shared regulation’. Despite a difference in names, both constructs refer to comparable regulation activities.

  2. The present study reports on secondary analyses on data which were originally collected for a previous study. The setting in which data were collected, is extensively described in De Backer et al. (2016). We therefore refer interested readers to this publication for more detailed information on the collaborative learning groups and the tasks students were engaged in.

  3. In total, nine groups of six students and two groups of five students were involved in the RPT-intervention. For the current study only groups of six students were selected, to increase comparability between groups. We more specifically made a random selection of five groups of six students, due to infrastructure-related limitations. Since the groups worked simultaneously in different classrooms and only a limited number of camera’s was available, only video recording of five groups could be made.

  4. We would like to clarify that the present study reports on secondary analyses of video data that were previously collected and coded in order to analyse time-bound evolutions in RPT-participants’ SSMR (see De Backer et al. 2015).The events of SSMR that were previously segmented, served as a starting point for the coding and analysis that was undertaken in the current study. Consequently, the first and the second step outlined in this paragraph are described in detail in De Backer et al. (2015), whereas the third step concerns coding that was exclusively (and from scratch) undertaken for the present study.

  5. It should be noted that the original classification of Roscoe and Chi (2008) refers to types of reactions (given by tutors and tutees) on the cognitive level (i.e. during knowledge construction). In the current study, we merely adopt the conceptualization of continuers, paraphrasing reactions, and elaborative reactions, but exclusively apply this to RPT-participants’ regulative reactions during events of SSMR.

  6. In the RPT_MCR coding instrument, task analysis, activation of prior knowledge, planning in advance, interim planning, comprehension monitoring, monitoring of progress, evaluation of learning outcomes, and evaluation of the learning process are distinguished as regulation strategies.

  7. For example, when a group participated in five events of SSMR that were identified as interrogative SSMR, but one of the students did not contribute to these events, his/her score would be zero, while a very active student who contributed in each of the events of interrogative SSMR with three metacognitive statements, would receive a score of 15.

  8. In the five-cluster model, two clusters merely differ in the probability of categories from the parameter ‘function of SSMR’, whereas the probabilities of all other parameters are quasi identical.

References

  • Cohen, J. (1977). Statistical power analysis for the behavioural sciences. New York: Academic Press.

    Google Scholar 

  • De Backer, L., Van Keer, H., & Valcke, M. (2015). Exploring evolutions in reciprocal peer tutoring groups’ socially shared metacognitive regulation and identifying its metacognitive correlates. Learning and Instruction, 38, 63–78. https://doi.org/10.1016/j.learninstruc.2015.04.001.

  • De Backer, L., Van Keer, H., Moerkerke, B., & Valcke, M. (2016). Examining evolutions in the adoption of metacognitive regulation in reciprocal peer tutoring groups. Metacognition and Learning, 11(2), 187–213. https://doi.org/10.1007/s11409-015-9141-7.

  • 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 (2ndedition) (pp. 83–106). New York: Routledge, Self-Regulation, Co-Regulation, and Shared Regulation in Collaborative Learning Environments.

  • Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. K. (2011). Socially shared metacognition in dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21, 379–393.

    Article  Google Scholar 

  • Iiskala, T., Volet, S., Lehtinen, E., & Vauras, M. (2015). Socially shared metacognitive regulation in asynchronous CSCL in science: Functions, evolution, and participation. Frontline Learning Research, 3, 78–111.

    Google Scholar 

  • Isohätälä, J., Järvenoja, H., & Järvelä, S. (2017). Socially shared regulation of learning and participation in social interaction in collaborative learning. International Journal of Educational Research, 81, 11–24.

    Article  Google Scholar 

  • Isohätälä, J., Näykki, P., & Järvelä, S. (2019). Cognitive and socio-emotional interaction in collaborative learning: Exploring fluctuations in students’ participation. Scandinavian Journal of Educational Research., 1–21. https://doi.org/10.1080/00313831.2019.1623310.

  • Järvelä, S., Järvenojä, H., Malmberg, J., & Hadwin, A. (2013). Exploring socially shared regulation in the context of collaboration. Journal of Cognitive Education and Psychology, 12, 267–286.

    Article  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, 1–11.

    Article  Google Scholar 

  • Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: Can it explain differences in students’ conceptual understanding? Metacognition and Learning, 9, 287–307.

    Article  Google Scholar 

  • Koivuniemi, M., Järvenoja, H., & Järvelä, S. (2018). Teacher education students’ strategic activities in challenging collaborative learning situations. Learning, Culture, and Social Interaction, 19, 109–123.

    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.

    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 low- and high-performing groups. Computers in Human Behavior, 52, 562–572.

    Article  Google Scholar 

  • Michinov, N., & Michinov, E. (2009). Investigating the relationship between transactive memory and performance in collaborative learning. Learning and Instruction, 19, 43–54.

    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, 31–46.

    Article  Google Scholar 

  • Panadero, E., & Järvelä, S. (2015). Socially shared regulation of learning: A review. European Psychologist, 20, 190–203.

    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, 375–415.

    Article  Google Scholar 

  • Rogiers, A., Merchie, E., De Smedt, F., De Backer, L., & Van Keer, H. (2020). A lifespan developmental perspective on strategic processing. In: D.L. Dinsmore, L.K. Fryer, M. M. Parkinson (Eds). Handbook of Strategies and Strategic Processing (pp.47-62). New York: Routledge.

  • Roscoe, R. D., & Chi, M. (2008). Tutor learning: The role of explaining and responding to questions. Instructional Science, 36, 321–350.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Schoor, C., Narciss, S., & Körndle, H. (2015). Regulation during cooperative and collaborative learning: A theory-based review of terms and concepts. Educational Psychologist, 50, 97–119.

    Article  Google Scholar 

  • Topping, K. J. (2005). Trends in peer learning. Educational Psychology, 25, 631–645.

    Article  Google Scholar 

  • Vermunt, J., & Magidson, J. (2003). Latent class models for classification. Computational Statistics and Data Analysis, 41, 531–537.

    Article  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, 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.

    Article  Google Scholar 

  • Vuopala, E., Näykki, P., Isohätälä, J., & Järvelä, S. (2019). Knowledge co-construction activities and task-related monitoring in scripted collaborative learning. Learning, Culture, and Social Interaction, 21, 234–249.

    Article  Google Scholar 

  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41, 64–70.

    Article  Google Scholar 

  • Zheng, J., Xing, W., & Zhu, G. X. (2019). Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment. Computers & Education, 136, 34–48.

    Article  Google Scholar 

Download references

Funding

The study was funded by the Special Research Fund of Ghent University under grant BOF17/PDO/025.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liesje De Backer.

Ethics declarations

The study involved human participants. Active written informed consent was given by them prior to data collection. The study was conducted in accordance with the General Ethical Protocol of the Ethical Committee of the Faculty of Psychology and Educational Sciences at Ghent University, Belgium.

Conflict of interest

The author’s declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1 Illustrations of the characteristics ‘trigger for SSMR, ‘level of elaboration in SSMR’, and ‘function of SSMR’

Appendix 1 Illustrations of the characteristics ‘trigger for SSMR, ‘level of elaboration in SSMR’, and ‘function of SSMR’

Trigger for SSMR

factual trigger

t1: “We are asked to develop a discovery learning activity. It means it should be based on the constructivist guidelines the tutor just explained, right?”

t4: “I guess… students should actively discover knowledge without much interference from the teacher. So that’s constructivist-based not so?”

t2: “Well there will be some guidance by the teacher, but that will take the form of hinting and scaffolding, not so? Certainly not strictly scripting students’ learning, right?”

T: “Yeah… that’s right. Scripting is too restrictive to be considered constructivist.”

thought-provoking trigger

t2: “I don’t think our answer is sufficient. We only focussed on reducing students’ extraneous cognitive load when constructing declarative knowledge, but the range of cognitivist constructs is much larger. I think we should also illustrate ‘dual channel theory’ and ‘advance organiser’ or at least all types of cognitive load.”

t3: “But we cannot integrate all theoretical constructs in our learning activity. I think our answer is sufficient, it’s narrowly focussed but it can correctly be called an illustration of cognitivism, as was requested.”

t4: “Maybe we can just add a second illustration which is focussed more on procedural knowledge? What we have so far is correct, I think, but I do agree that we could have opted for more variation in the illustrations we provided.”

t2: “But even then the problem remains that our answer is too narrowly focussed. I agree with tutee #2. We didn’t do anything with half of the theoretical concepts. So I really think our answer is currently of poor quality and even if we add an illustration based on procedural knowledge, it will still not be what the lecturer wants to read.”

Level of elaboration in students’ regulative reactions during SSMR

continuer

T: “So far we have completed two subtasks and we still have 30 min. Remaining for evaluating the session. Maybe we can review our answer on the first subtask before we start evaluating, because tutee 3 felt it was incomplete?”

t4: “Okay. Fine by me.”

t2: “Perfect!”

paraphrase

T: “This session is about instructional cognitivism. What do you know about it? Which concepts can you recall from the theoretical lecture?

t1: “Extraneous cognitive load? Due to limited capacity of the working memory. And the importance of clear presentations?”

t2: “Yes! Presenting information in keywords, schemes, and Mind Maps such that it becomes manageable and does not overload the working memory.”

t5: “Grouping information in chunks to avoid overload of the working memory. Working memory can handle seven chunks.”

elaboration

t2: “Is the third statement not an example of scaffolding?”

t3: “But the help is given by a computer-supported tool. And it concerns very strict instructions such that the student merely executes what the tool instructs. Isn’t that more an example of the learning machines from instructional behaviourism?”

 

t5: “I am not sure about the learning machines but I don’t see scaffolding indeed. There is no discovery learning and was scaffolding not always aimed at discovery learning? But in statement 3 students don’t decide what to discover, the tool does.”

 

t1: “And the tool immediately informs whether students’ answer is right or wrong. I do think that is behaviourism. Something like feedback, contingent feedback, no?”

Function of SSMR in the collaborative learning process

confirm CL

T: “I suggest that we check our final answer before closing the session.”

t4: “Good idea because I am unsure whether I sufficiently integrated all the things that we discussed in our answers. A final check to control for mistakes is needed.”

t2: “We can also check whether the answers are in line with the learning objectives.”

t1: “Is it okay if I start reading the objectives and then we check statement by statement whether our answer is correct and if something is missing?”

T: “Okay, let’s start.”

activate CL

T: When I say ‘assessment’, then what is the first thing you are thinking about? Or what do you remember from the lecture last week?”

t1: “Marking process and product.”

t5:“Summative versus formative. Assessment by the teacher, a peer, or oneself.”

t3: “It really helps to freshen up concepts that we have seen before. It helps me understand the new terminology.”

T: “On top of that, I think it is also good to think about how we solved the previous session, which went smoothly. I think that was because we took time to explain and fully grasp the theory before making the assignment.”

t4: “For me it was also helpful that each of us was given a different concept by the peer tutor and was asked to come up with an example of our own at the end of the session.”

t5: “Yeah, it would be good to include such an exercise in this session as well.”

slow down CL

t2: “In the fourth example the teacher makes use of portfolio-based evaluation. We said that it was formative assessment. I am not sure but isn’t it also process evaluation?”

t4: “I don’t think so because the portfolio implies interim evaluation but it is still focussed on the outcomes. So I would think that it is an example of product evaluation… on a formative base. No?”

t3: “I’m not sure … but your explanation makes sense. The portfolio doesn’t shed light on the learning process, does it? So it would be strange to call it process evaluation, not so?”

t2: “I mistakenly assumed that formative assessment and process evaluation are the same, but I guess they are not. The portfolio indeed evaluates the learning products. So we stick to our answer that it is an example of formative product assessment?”

t3: “I would do so.”

change CL

T: “Let’s first summarize why some of us think that rubrics are behavioristic and why others think they are not.”

t2: “Rubrics operationalize behavior in small steps. And small steps, operational learning objectives are behavioristic, not so?

t4: “But they are used as formative assessment techniques, not so? That why we think that they are constructivist.”

t1: “I really think that we should end this conversation. We have few time left for the remaining of the session and on top of that, we are now discussing something which will not even be included in our answer. The question was whether the illustration meets the standards of a good rubric.

t5: “I actually agree with tutee #1. It is interesting to gain deeper insight and to elaborate on the items that are mentioned in the questions but we should focus on solving the questions and writing down our answers to the questions. The assistant will evaluate our work based on our answers and our report, not on the discussions we have here.”

t3: “That’s right. Let’s move over to the next sub-task instead of continuing this discussion.”

stop CL

[T, t1, t2, and t3 are discussing their conflicting understanding of the concept direct instruction’]

t4: “I think we should continue with the last sub-task. We only have 15 min left.”

t3: “Is it already that late?”

t5: “Uhum.”

t3: “Okay then.”

[T, t1, t2, and t3 heard the comments by their group mates but continue their discussion]

  1. Note: T = tutor, t = tutee, CL = collaborative learning. Although the examples in A frequently concern events of SSMR that are initiated by the peer tutor, it should be noted that only 36.2% of the events of SSMR segmented in the present study is tutor-initiated whereas 63.8% is tutee-initiated

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Backer, L., Van Keer, H. & Valcke, M. Variations in socially shared metacognitive regulation and their relation with university students’ performance. Metacognition Learning 15, 233–259 (2020). https://doi.org/10.1007/s11409-020-09229-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11409-020-09229-5

Keywords

Navigation