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Exploring turn-taking patterns during dialogic collaborative problem solving

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Abstract

This study investigated students’ turn-taking patterns during dialogic collaborative problem solving, with analysis based on the participation-shift analytical framework. 168 primary fourth-grade students were assigned to 42 groups and worked on three mathematical problems for a total of 30 minutes. Group-level analysis revealed that most students accessed the conversational floor by receiving it from the last speaker. Usurping a floor offered to another person and claiming a floor opened to the whole group were positively associated with the intensity and the balance of group discussion. Individual-level analysis further identified four latent profiles of individuals with distinct turn-taking styles: turn-receivers (i.e., receiving the floor assigned by the last speaker) (15%), turn-usurpers (20%) (usurping the floor when it was offered to another person), turn-claimers (10%) (claiming the floor when it was opened to the whole group) and turn-balancers (55%) (no strong turn-taking tendency). Individual participation rates and prior Chinese grades proved to be the two most significant unique predictors of individual membership in the turn-usurper profile. The findings suggest ensuring students’ equitable access to the conversational floor and provide teachers with several specific turn-taking related approaches to promote equity and respect in peer talk.

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References

  • Avcı, Ü. (2020). Examining the role of sentence openers, role assignment scaffolds and self-determination in collaborative knowledge building. Educational Technology Research and Development, 68(1), 109–135. https://doi.org/10.1007/s11423-019-09672-5

    Article  Google Scholar 

  • Bakhtin, M. M. (1999). Problems of Dostoevsky’s poetics. University of Minnesota Press.

  • Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359. https://doi.org/10.1207/S15327809JLS1203_1

    Article  Google Scholar 

  • Belland, B. R., Kim, C. M., & Hannafin, M. J. (2013). A framework for designing scaffolds that improve motivation and cognition. Educational Psychologist, 48(4), 243–270. https://doi.org/10.1080/00461520.2013.838920

    Article  Google Scholar 

  • Blatchford, P., Kutnick, P., Baines, E., & Galton, M. (2003). Toward a social pedagogy of classroom group work. International Journal of Educational Research, 39(1–2), 153–172. https://doi.org/10.1016/S0883-0355(03)00078-8

    Article  Google Scholar 

  • Blau, P. M. (1964). Exchange and power in social life. Wiley.

  • Blue, A. V., Stratton, T. D., Donnelly, M. B., Nash, P. P., & Schwartz, R. W. (1998). Students’ communication apprehension and its effects on PBL performance. Medical Teacher, 20(3), 217–221.

    Article  Google Scholar 

  • Boaler, J. (2008). Promoting ‘relational equity’ and high mathematics achievement through an innovative mixed-ability approach. British Educational Research Journal, 34(2), 167–194. https://doi.org/10.1080/01411920701532145

    Article  Google Scholar 

  • Borge, M. & Carroll, J. M. (2014). Verbal equity, cognitive specialization, and performance. In Proceedings of the 18th international conference on supporting group work (pp. 215–225). New York: ACM.

  • Borge, M., Ong, Y. S., & Rosé, C. P. (2018). Learning to monitor and regulate collective thinking processes. International Journal of Computer-Supported Collaborative Learning, 13(1), 61–92.

    Article  Google Scholar 

  • Cela, K. L., Sicilia, M. Á., & Sánchez, S. (2015). Social network analysis in e-learning environments: A preliminary systematic review. Educational Psychology Review, 27(1), 219–246. https://doi.org/10.1007/s10648-014-9276-0

    Article  Google Scholar 

  • Chen, B., Resendes, M., Chai, C. S., & Hong, H. Y. (2017). Two tales of time: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interactive Learning Environments, 25(2), 162–175.

    Article  Google Scholar 

  • Chen, G., Chiu, M. M., & Wang, Z. (2012). Predicting social cues during online discussions: Effects of evaluations and knowledge content. Computers in Human Behavior, 28(4), 1497–1509. https://doi.org/10.1016/j.chb.2012.03.017

    Article  Google Scholar 

  • Chen, G., Lo, C. K., & Hu, L. (2020). Sustaining online academic discussions: Identifying the characteristics of messages that receive responses. Computers & Education, 156, 103938.

  • Chi, M. T., Adams, J., Bogusch, E. B., Bruchok, C., Kang, S., Lancaster, M., Levy, R., Li, N., McEldoon, K. L., Stump, G. S., Wylie, R., & Yaghmourian, D. L. (2018). Translating the ICAP theory of cognitive engagement into practice. Cognitive Science, 42(6), 1777–1832.

    Article  Google Scholar 

  • Choi, H., & Kang, M. (2010). Applying an activity system to online collaborative group work analysis. British Journal of Educational Technology, 41(5), 776–795. https://doi.org/10.1111/j.1467-8535.2009.00978.x

    Article  Google Scholar 

  • Clark, H. H., & Schaefer, E. F. (1989). Contributing to discourse. Cognitive Science, 13(2), 259–294.

    Article  Google Scholar 

  • Cohen, E. G., & Lotan, R. A. (2014). Designing groupwork: Strategies for the heterogeneous classroom (3rd ed.). Teachers College Press.

  • Csanadi, A., Eagan, B., Kollar, I., Shaffer, D. W., & Fischer, F. (2018). When coding-and-counting is not enough: Using epistemic network analysis (ENA) to analyze verbal data in CSCL research. International Journal of Computer-Supported Collaborative Learning, 13(4), 419–438. https://doi.org/10.1007/s11412-018-9292-z

    Article  Google Scholar 

  • Database of Mathematical Olympiad (n.d.). Mathematical Olympiad problems for fifth grade students. Retrieved March 20, 2019, from https://www.aoshuku.com/timu/23064.html.

  • De Laat, M., Lally, V., Lipponen, L., & Simons, R. J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2(1), 87–103. https://doi.org/10.1007/s11412-007-9006-4

    Article  Google Scholar 

  • De Wever, B., Van Keer, H., Schellens, T., & Valcke, M. (2010). Roles as a structuring tool in online discussion groups: The differential impact of different roles on social knowledge construction. Computers in Human Behavior, 26(4), 516–523.

    Article  Google Scholar 

  • Dyke, G., Kumar, R., Ai, H. & Rosé, C. P. (2012). Challenging assumptions: Using sliding window visualizations to reveal time-based irregularities in CSCL processes. In Proceedings of the 10th international conference of the learning sciences (Vol. 1, pp. 363–370). Sydney, Australia: ISLS.

  • Engle, R. A., Langer-Osuna, J. M., & McKinney de Royston, M. (2014). Toward a model of influence in persuasive discussions: Negotiating quality, authority, privilege, and access within a student-led argument. Journal of the Learning Sciences, 23(2), 245–268. https://doi.org/10.1080/10508406.2014.883979

    Article  Google Scholar 

  • Fu, E. L., van Aalst, J., & Chan, C. K. (2016). Toward a classification of discourse patterns in asynchronous online discussions. International Journal of Computer-Supported Collaborative Learning, 11(4), 441–478.

    Article  Google Scholar 

  • Gergen, K. J., Greenberg, M., & Willis, R. H. (1980). Social exchange: Advances in theory and research. Plenum.

  • Gibson, D. R. (2003). Participation shifts: Order and differentiation in group conversation. Social Forces, 81(4), 1335–1380.

    Article  Google Scholar 

  • Gibson, D. R. (2005). Taking turns and talking ties: Networks and conversational interaction. American Journal of Sociology, 110(6), 1561–1597. https://doi.org/10.1086/428689

    Article  Google Scholar 

  • Gibson, W. A. (1959). Three multivariate models: Factor analysis, latent structure analysis, and latent profile analysis. Psychometrika, 24(3), 229–252.

    Article  Google Scholar 

  • Gillies, R. M. (2019). Promoting academically productive student dialogue during collaborative learning. International Journal of Educational Research, 97, 200–209.

    Article  Google Scholar 

  • Heo, H., Lim, K. Y., & Kim, Y. (2010). Exploratory study on the patterns of online interaction and knowledge co-construction in project-based learning. Computers and Education, 55(3), 1383–1392. https://doi.org/10.1016/j.compedu.2010.06.012

    Article  Google Scholar 

  • Hu, L. (2021). Turn-usurping in dialogic collaborative problem solving. In Proceedings of 15th international conference of the learning sciences (ICLS) (pp. 59–66). Bochum, Germany: ISLS.

  • Järvelä, S., Järvenoja, H., & Malmberg, J. (2019). Capturing the dynamic and cyclical nature of regulation: Methodological progress in understanding socially shared regulation in learning. International Journal of Computer-Supported Collaborative Learning, 14(4), 425–441.

    Article  Google Scholar 

  • Jin, J. (2012). Silence in small group interactions for problem-based learning at an English-medium university in Asia [Doctoral thesis, University of Hong Kong]. HKU Theses Online (HKUTO). Retrieved from http://hdl.handle.net/10722/173960.

  • Johnson, D. W., & Johnson, R. (2016). Cooperative learning and teaching citizenship in democracies. International Journal of Educational Research, 76, 162–177. https://doi.org/10.1016/j.ijer.2015.11.009

    Article  Google Scholar 

  • Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669

    Article  Google Scholar 

  • Kapur, M., Voiklis, J., & Kinzer, C. K. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers and Education, 51(1), 54–66. https://doi.org/10.1016/j.compedu.2007.04.007

    Article  Google Scholar 

  • King, A. (2008). Structuring peer interaction to promote higher-order thinking and complex learning in cooperating groups. In R. M. Gillies, A. Ashman, & J. Terwel (Eds.), The teacher’s role in implementing cooperative learning in the classroom (pp. 73–92). Springer.

  • La Greca, A. M., & Stone, W. L. (1993). Social anxiety scale for children—revised: Factor structure and concurrent validity. Journal of Clinical Child Psychology, 22(1), 17–27.

    Article  Google Scholar 

  • Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.

    Article  Google Scholar 

  • Leenders, R. T. A. J., Contractor, N. S., & DeChurch, L. A. (2016). Once upon a time: Understanding team processes as relational event networks. Organizational Psychology Review, 6(1), 92–115. https://doi.org/10.1177/2041386615578312

    Article  Google Scholar 

  • Lemke, J. L. (1990). Talking science: Language, learning, and values. Ablex.

  • Levinson, S. C. (1983). Pragmatics. Cambridge University Press.

  • Lewis, C. M. & Shah, N. (2015). How equity and inequity can emerge in pair programming. In International computing education research workshop (pp. 41–50). New York, NY: Association of Computing Machinery (ACM).

  • Liu, L., Hao, J., von Davier, A. A., Kyllonen, P., & Zapata-Rivera, J.-D. (2015). A tough nut to crack: Measuring collaborative problem solving. In Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.), Handbook of research on computational tools for real-world skill development. IGI-Global.

  • Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21–39.

    Article  Google Scholar 

  • Marcos-García, J. A., Martínez-Monés, A., & Dimitriadis, Y. (2015). DESPRO: A method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations. Computers and Education, 82, 335–353. https://doi.org/10.1016/j.compedu.2014.10.027

    Article  Google Scholar 

  • Martı́nez, A., Dimitriadis, Y., Rubia, B., Gómez, E., & de la Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers and Education, 41(4), 353–368.

    Article  Google Scholar 

  • Matusov, E. (2009). Journey into dialogic pedagogy. Hauppauge, NY: Nova Publishers.

  • Mayer, S. J. (2012). Classroom discourse and democracy: Making meanings together. Peter Lang.

  • Mercer, N., Wegerif, R., & Major, L. (2019). The Routledge international handbook of research on dialogic education. Routledge.

  • Miller, M., & Hadwin, A. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 52, 573–588. https://doi.org/10.1016/j.chb.2015.01.050

    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. https://doi.org/10.1007/s11409-013-9105-8

    Article  Google Scholar 

  • Molinari, L., Mameli, C., & Gnisci, A. (2013). A sequential analysis of classroom discourse in Italian primary schools: The many faces of the IRF pattern. British Journal of Educational Psychology, 83(3), 414–430.

    Article  Google Scholar 

  • Mullis, I. V. S., & Martin, M. O. (Eds.). (2013). TIMSS 2015 assessment frameworks. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.

  • Näykki, P., Isohätälä, J., Järvelä, S., Pöysä-Tarhonen, J., & Häkkinen, P. (2017). Facilitating socio-cognitive and socio-emotional monitoring in collaborative learning with a regulation macro script—An exploratory study. International Journal of Computer-Supported Collaborative Learning, 12(3), 251–279.

    Article  Google Scholar 

  • Noroozi, O., Weinberger, A., Biemans, H. J. A., Mulder, M., & Chizari, M. (2013). Facilitating argumentative knowledge construction through a transactive discussion script in CSCL. Computers and Education, 61(1), 59–76. https://doi.org/10.1016/j.compedu.2012.08.013

    Article  Google Scholar 

  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569.

    Article  Google Scholar 

  • Rosenberg, J. M., van Lissa, C. J., Beymer, P. N., Anderson, D. J., Schell, M. J. & Schmidt, J. A. (2019). tidyLPA: Easily carry out latent profile analysis (LPA) using open-source or commercial software [R package]. Retrieved from https://data-edu.github.io/tidyLPA/

  • Rosenholtz, S. J. (1985). Treating problems of academic status. In J. Berger & M. Zelditch (Eds.), Status, rewards, and influence (pp. 445–470). Jossey-Bass.

  • Saab, N., Van Joolingen, W. R., & van Hout-Wolters, B. H. (2007). Supporting communication in a collaborative discovery learning environment: The effect of instruction. Instructional Science, 35(1), 73–98.

    Article  Google Scholar 

  • Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50, 696–735.

    Article  Google Scholar 

  • Schegloff, E. A. (2007). Sequence organization in interaction: A primer in conversation analysis I (Vol. 1). Cambridge University Press.

  • Schnaubert, L., & Bodemer, D. (2019). Providing different types of group awareness information to guide collaborative learning. International Journal of Computer-Supported Collaborative Learning, 14(1), 7–51. https://doi.org/10.1007/s11412-018-9293-y

    Article  Google Scholar 

  • Shah, N., & Lewis, C. M. (2019). Amplifying and attenuating inequity in collaborative learning: Toward an analytical framework. Cognition and Instruction, 37(4), 423–452. https://doi.org/10.1080/07370008.2019.1631825

    Article  Google Scholar 

  • Siebert-Evenstone, A. L., Irgens, G. A., Collier, W., Swiecki, Z., Ruis, A. R., & Shaffer, D. W. (2017). In search of conversational grain size: Modelling semantic structure using moving stanza windows. Journal of Learning Analytics, 4(3), 123–139. https://doi.org/10.18608/jla.2017.43.7

    Article  Google Scholar 

  • Slavin, R. E., Lake, C., Hanley, P., & Thurston, A. (2014). Experimental evaluations of elementary science programs: A best-evidence synthesis. Journal of Research in Science Teaching, 51(7), 870–901. https://doi.org/10.1002/tea.21139

    Article  Google Scholar 

  • Stegmann, K., Wecker, C., Weinberger, A., & Fischer, F. (2012). Collaborative argumentation and cognitive elaboration in a computer-supported collaborative learning environment. Instructional Science, 40(2), 297–323.

    Article  Google Scholar 

  • Sterba, S. K. (2013). Understanding linkages among mixture models. Multivariate Behavioral Research, 48(6), 775–815.

    Article  Google Scholar 

  • Stevens, R. (2012). Charting neurodynamic eddies in the temporal flows of teamwork. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 56, No. 1, pp. 208–212). Thousand Oaks: SAGE Publications.

  • Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55.

  • TIMSS & PIRLS International Study Center. (2015). TIMSS 2015 Item Information Tables–Fourth Grade. Retrieved from https://timssandpirls.bc.edu/timss2015/international-database/downloads/T15_G4_ItemInformation.zip.

  • Tsvetkova, M., Garciá-Gavilanes, R., & Yasseri, T. (2016). Dynamics of disagreement: Large-scale temporal network analysis reveals negative interactions in online collaboration. Scientific Reports, 6(1), 1–10. https://doi.org/10.1038/srep36333

    Article  Google Scholar 

  • Wang, Q. (2009). Design and evaluation of a collaborative learning environment. Computers & Education, 53(4), 1138–1146. https://doi.org/10.1016/j.compedu.2009.05.023

    Article  Google Scholar 

  • Webb, N. M., Franke, M. L., De, T., Chan, A. G., Freund, D., Shein, P., & Melkonian, D. K. (2009). ‘Explain to your partner’: Teachers’ instructional practices and students’ dialogue in small groups. Cambridge Journal of Education, 39(1), 49–70. https://doi.org/10.1080/03057640802701986

    Article  Google Scholar 

  • Wiltshire, T. J., Butner, J. E., & Fiore, S. M. (2018). Problem-solving phase transitions during team collaboration. Cognitive Science, 42(1), 129–167.

    Article  Google Scholar 

  • Wise, A. F., & Chiu, M. M. (2011). Analyzing temporal patterns of knowledge construction in a role-based online discussion. International Journal of Computer-Supported Collaborative Learning, 6(3), 445–470. https://doi.org/10.1007/s11412-011-9120-1

    Article  Google Scholar 

  • Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2010). Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), 686–688.

    Article  Google Scholar 

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Acknowledgements

This research was supported by Hong Kong RGC grant No. 17608318.

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  1. Faculty of Education, The University of Hong Kong, Pokfulam Road, Hong Kong, 999077, China

    Liru Hu & Gaowei Chen

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Hu, L., Chen, G. Exploring turn-taking patterns during dialogic collaborative problem solving. Instr Sci 50, 63–88 (2022). https://doi.org/10.1007/s11251-021-09565-2

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