Extending past research showing that regulative activities (metacognitive and relational) can aid learning, this study tests whether sequences of cognitive, metacognitive and relational activities affect subsequent cognition. Scaffolded by a computer avatar, 54 primary school students (working in 18 groups of 3) discussed writing a report about a foreign country for 51,338 turns. Statistical discourse analysis (SDA) of these sequences of talk showed that after low cognition, high cognition, planning or evaluation, both low and high cognition were more likely (some effects lasted 6 conversation turns). After monitoring or positive relational activities (confirm, engage), low cognition was more likely. After a denial however, high cognition was less likely. These results suggest that metacognitive planning organizes subsequent cognitive activities and facilitates the transition between acquisition of knowledge and meaning making, while relational activities help enact them. These insights can inform micro-temporal theories of social regulation and shared knowledge construction.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Azevedo, R., & Green, J. A. (2010). The measurement of learners’self-regulated cognitive and metacognitive processes while using computer-based learning enviroments. Educational Psychologist, 45, 203–209.
Baker, M. J., de Vries, E., Lund, K., & Quignard, M. (2001). Computer-mediated epistemic interactions for co-constructing scientific notions. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), Proceedings of EuroCSCL 2001 (pp. 89–96). Maastricht: Maastricht McLuhan Institute.
Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.
Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93, 491–507.
Burgoon, J. K., Dillman, L., & Stern, L. (1993). Adaptation in dyadic interaction. Communication Theory, 3, 295–316.
Chen, G., Chiu, M. M., & Wang, Z. (2012). Social metacognition and the creation of correct, new ideas: A statistical discourse analysis of online mathematics discussions. Computers in Human Behavior, 28(3), 868–880.
Chi, M. (2009). Active-constructive-interactive. Topics in Cognitive Science, 1(1), 73–105.
Chi, M.T.H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. (2001). Learning from human tutoring. Cognitive Science, 25, 471–534.
Chiu, M. M. (2008). Flowing toward correct contributions during groups’ mathematics problem solving: A statistical discourse analysis. Journal of the Learning Sciences, 17(3), 415–463.
Chiu, M. M., & Khoo, L. (2003). Rudeness and status effects during group problem solving. Journal of Educational Psychology, 95, 506–523.
Chiu, M. M., & Khoo, L. (2005). A new method for analyzing sequential processes: Dynamic multi-level analysis. Small Group Research, 36, 600–631.
Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, R. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition. Washington, DC: American Psychological Association.
Cohen, E. G. (1994). Restructuring the classroom: conditions for productive small groups. Review of Educational Research, 64(1), 1–35.
Craik, F., & Lockhart, R. (1972). Levels of processing: a framework for memory research. Journal of Verbal Thinking and Verbal Behavior, 11, 671–684.
Davis, E. A., & Linn, M. (2000). Scaffolding students’ knowledge integration; Prompts for reflection in KIE. International Journal of Science Education, 22(8), 819–837.
De Bruin, A. B. H., Theide, K. W., Camp, G., & Redford, J. (2011). Generating keywords improves metacomprehension and self-regulation in elementary and middle school children. Journal of Experimental Child Psychology, 109(3), 294–310.
Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative-learning (pp. 1–19). Oxford: Elsevier.
Efklides, A. (2008). Metacognition. European Psychologist, 13, 277–287.
Flavell, J. H. (1979). Metacognition and cognitive monitoring: a new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911.
Fleiss, J. (1981). Statistical examples for rates and proportions. New York: Wiley.
Franke, R. H., & Kaul, J. D. (1978). The Hawthorne experiments: first statistical interpretation. American Sociological Review, 43, 623–643.
Gee, J. P. (2005). An introduction to discourse analysis: Theory and method. London: Routledge.
Goldstein, H. (1995). Multilevel statistical models. Sydney: Edward Arnold.
Hadwin, A., & Oshige, M. (2011). Self-regulation, co-regulation, and socially shared regulation. Teachers College Record, 113(6).
Huedo-Medina, T. B., Sanchez-Meca, J., Marin-Martinez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods, 11, 193–206.
Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition within primary school pupil dyads’ collaborative processes. Learning and Instruction, 21, 379–393.
Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar G. (2012). Task-related and social regulation during online collaborative learning. Metacognition and Learning, 7(1), 25–43.
Järvelä, S., & Hadwin, A. (2013). New frontiers: regulating learning in CSCL. Educational Psychologist, 48, 25–39.
Jehn, K. A., & Shah, P. P. (1997). Interpersonal relationships and task performance: an examination of mediation processes in friendship and acquaintance groups. Journal of Personality and Social Psychology, 72, 775–790.
Kempler, T. M., & Linnenbrink, E. A. (2006). Helping behaviors in collaborative groups in math: A descriptive analysis. In S. Karabenick & R. Newman (Eds.), Help seeking in academic settings: Goals, groups, and context (pp. 89–115). Mahwah: Erlbaum.
Kennedy, P. (2008). A guide to econometrics. Cambridge: Blackwell.
King, A. (1998). Transactive peer tutoring: distributing cognition and metacognition. Educational Psychology Review, 10(1), 57–74.
King, A. (2002). Structuring peer interaction to promote high-level cognitive processing. Theory Into Practice, 41(1), 33–39.
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9, 137–163.
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, 335–353.
Krippendorff, K. (2004). Content analysis. Thousand Oaks: Sage.
Lin, L., & Zabrucky, K. M. (1998). Calibration of comprehension: research and implications for education and instruction. Contemporary Educational Psychology, 23, 345–391.
Lu, J., Chiu, M. M., & Law, N. (2011). Collaborative argumentation and justifications: A statistical discourse analysis of online discussions. Computers in Human Behavior, 27, 946–955.
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect. Multivariate Behavioral Research, 39, 99–128.
Massey, A. P., Montoya-Weiss, M. M., & Hung, Y. (2003). Because time matters: temporal coordination in global virtual teams. Journal of Management Information Systems, 19, 129–155.
McGrath, J. E. (1991). Time, interaction, and performance (TIP): a theory of groups. Small Group Research, 22(2), 147–174.
Meijer, J., Veenman, M. V., & van Hout-Wolters, B. H. (2006). Metacognitive activities in text-studying and problem-solving: development of a taxonomy. Educational Research and Evaluation, 12(3), 209–237.
Molenaar, I. (2003). Exploration-net: Online collaboration. In D. Lassner & C. McNaught (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 398–400). Chesapeake, VA: AACE.
Molenaar, I., & Roda, C. (2008). Attention management for dynamic and adaptive scaffolding. Pragmatics & Cognition, 16(2), 224–271.
Molenaar, I., Chiu, M. M., Sleegers, P. J. C., & van Boxtel, C.A.M. (2011a). Scaffolding of Small Groups’ Metacognitive Activities with an Avatar. International Journal of Computer Supported Collaborative Learning, 6(4), 601–624.
Molenaar, I., van Boxtel, C. A. M., & Sleegers, P.J.C. (2011b). Metacognitive Scaffolding in an Innovative Learning Arrangement. Instructional Science, 39(6), 785–803.
Nelson, T. O. (1996). Consciousness and metacognition. American Psychologist, 51, 102–116.
Nijstad, B. A., Diehl, M., & Stroebe, W. (2003). Cognitive stimulation and interference in idea generating groups. In P. B. Paulus & B. A. Nijstad (Eds.), Group creativity: Innovation through collaboration (pp. 137–159). New York: Oxford University Press.
Pieschl, S. (2009). Metacognitive calibration - an extended conceptualization and potential applications. Metacognition and Learning, 4(1), 3–31.
Reimann, P. (2009). Time is precious. International Journal of Computer-Supported Collaborative Learning, 3, 239–257.
Reiser, B. J. (2004). Scaffolding complex learning: the mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304.
Salomon, G. (1993). Distributed cognitions. Cambridge: Cambridge University Press.
Van Boxtel, C. (2004). Studying peer interaction from three perspectives. In J. L. van der Linden & P. Renshaw (Eds.), Dialogic learning (pp. 125–144). Dordrecht: Kluwer.
Veenman, M. V. J. (2005). The assessment of metacognitive skills. In C. Artelt & B. Moschner (Eds.), Lernstrategien und Metakognition: Implikationen für Forschung und Praxis (pp. 75–97). Berlin: Waxmann.
Veldhuis-Diermanse, A. E. (2002). CSCLearning? Participation, learning activities and knowledge construction in computer-supported collaborative learning in higher education. Unpublished PhD thesis, Wageningen University, The Netherlands.
Volet, S., Vauras, M., & Salonen, P. (2009a). Self- and social regulation in learning contexts: an integrative perspective. Educational Psychologist, 44(4), 215–226.
Volet, S. E., Summers, M., & Thurman, J. (2009b). High-level co-regulation in collaborative learning. Learning and Instruction, 19(2), 128–143.
Vygotsky, L. S. (1978). Mind in society. Cambridge: Harvard University Press.
Webb, N. M., Nemer, K. M., & Zuniga, S. (2002). Short circuits or superconductors? American Educational Research Journal, 39, 943–989.
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46, 71–95.
Wilson, J. M., Straus, S. G., & McEvily, B. (2006). All in due time. Organizational Behavior and Human Decision Processes, 99, 16–33.
Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45, 267–276.
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: Lawrence Erlbaum Associates.
Wise, A., & Chiu, M. M. (2011). Analyzing temporal patterns of knowledge construction in a role-based online discussion. International Journal of Computer-Supported Collaborative Learning, 6, 445–470.
Wood, D., Bruner, J., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 17, 89–100.
Zimmerman, B. J. (2002). Becoming a self-regulated learner: an overview. Theory into Practice, 42(2), 64–70.
About this article
Cite this article
Molenaar, I., Chiu, M.M. Dissecting sequences of regulation and cognition: statistical discourse analysis of primary school children’s collaborative learning. Metacognition Learning 9, 137–160 (2014). https://doi.org/10.1007/s11409-013-9105-8
- Temporal analysis
- Collaborative learning
- Process analysis
- Elementary education
- Discourse analysis