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Statistical Discourse Analysis of Online Discussions: Informal Cognition, Social Metacognition, and Knowledge Creation

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Knowledge Creation in Education

Part of the book series: Education Innovation Series ((EDIN))

Abstract

To statistically model large data sets of sequences of knowledge processes during asynchronous, online forums, we must address analytic difficulties involving the whole data set (missing data, nested data, and the tree structure of online messages), dependent variables (multiple, infrequent, discrete outcomes and similar adjacent messages), and explanatory variables (sequences, indirect effects, false-positives, and robustness). Statistical discourse analysis (SDA) addresses all of these issues, as shown in an analysis of 1,330 asynchronous messages written by 17 students during a 13-week online educational technology course. The results showed how attributes at multiple levels (individual and message) affected knowledge creation processes. Men were more likely than women to theorize. Asynchronous messages created a micro-time context; opinions and asking about purpose preceded new information; and anecdotes, opinions, different opinions, elaborating ideas, and asking about purpose or information preceded theorizing. These results show how informal thinking precedes formal thinking and how social metacognition affects knowledge creation.

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References

  • Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93, 491–507.

    Article  Google Scholar 

  • Bereiter, C. (1994). Implications of postmodernism for science, or science as progressive discourse. Educational Psychologist, 29(1), 3–12.

    Article  Google Scholar 

  • Bereiter, C. (2002). Education and mind in the knowledge age. Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Bereiter, C., & Scardamalia, M. (2006). Education for the knowledge age: Design-centered models of teaching and instruction. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 695–713). Mahwah: Lawrence Erlbaum.

    Google Scholar 

  • Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models. London: Sage.

    Google Scholar 

  • Chen, G., & Chiu, M. M. (2008). Online discussion processes. Computers & Education, 50(3), 678–692.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chiu, M. M. (1996). Exploring the origins, uses and interactions of student intuitions: Comparing the lengths of paths. Journal for Research in Mathematics Education, 27(4), 478–504.

    Article  Google Scholar 

  • Chiu, M. M. (2000). Group problem solving processes: Social interactions and individual actions. Journal for the Theory of Social Behavior, 30(1), 27–50.

    Article  Google Scholar 

  • Chiu, M. M. (2001). Analyzing group work processes: Towards a conceptual framework and systematic statistical analyses. In F. Columbus (Ed.), Advances in psychology research (Vol. 4, pp. 193–222). Huntington: Nova Science.

    Google Scholar 

  • Chiu, M. M. (2008a). Effects of argumentation on group micro-creativity: Statistical discourse analyses of algebra students’ collaborative problem solving. Contemporary Educational Psychology, 33, 382–402.

    Article  Google Scholar 

  • Chiu, M. M. (2008b). Flowing toward correct contributions during group problem solving: A statistical discourse analysis. Journal of the Learning Sciences, 17(3), 415–463.

    Article  Google Scholar 

  • Chiu, M. M., & Khoo, L. (2003). Rudeness and status effects during group problem solving: Do they bias evaluations and reduce the likelihood of correct solutions? Journal of Educational Psychology, 95, 506–523.

    Article  Google Scholar 

  • Chiu, M. M., & Khoo, L. (2005). A new method for analyzing sequential processes: Dynamic multi-level analysis. Small Group Research, 36, 1–32.

    Article  Google Scholar 

  • Chiu, M. M., & Kuo, S. W. (2009). From metacognition to social metacognition: Similarities, differences, and learning. Journal of Education Research, 3(4), 1–19.

    Google Scholar 

  • Fujita, N. (2009). Group processes supporting the development of progressive discourse in online graduate courses. Unpublished Doctoral Dissertation, University of Toronto, Toronto. Retrieved from http://hdl.handle.net/1807/43778

  • Glassner, A., Weinstoc, M., & Neuman, Y. (2005). Pupils’ evaluation and generation of evidence and explanation in argumentation. British Journal of Educational Psychology, 75, 105–118.

    Article  Google Scholar 

  • Goldstein, H. (1995). Multilevel statistical models. Sydney: Edward Arnold.

    Google Scholar 

  • Goldstein, H., Healy, M., & Rasbash, J. (1994). Multilevel models with applications to repeated measures data. Statistics in Medicine, 13, 1643–1655.

    Article  Google Scholar 

  • Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26, 499–510.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Hacker, D. J., & Bol, L. (2004). Metacognitive theory. In D. M. McInerney & S. Van Etten (Eds.), Big theories revisited (Vol. 4, pp. 275–297). Greenwich: Information Age.

    Google Scholar 

  • Hakkarainen, K. (2003). Emergence of progressive-inquiry culture in computer-supported collaborative learning. Learning Environments Research, 6(2), 199–220.

    Article  Google Scholar 

  • Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28, 115–152.

    Article  Google Scholar 

  • Howe, C. (2009). Collaborative group work in middle childhood. Human Development, 52(4), 215–239.

    Article  Google Scholar 

  • Huedo-Medina, T. B., Sanchez-Meca, J., Marin-Martinez, F., & Botella, J. (2006). Assessing heterogeneity in meta-analysis. Psychological Methods, 11, 193–206.

    Article  Google Scholar 

  • Kennedy, P. (2008). Guide to econometrics. Cambridge: Wiley-Blackwell.

    Google Scholar 

  • King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9, 137–163.

    Article  Google Scholar 

  • Lee, E., Chan, C., & van Aalst, J. (2006). Students assessing their own collaborative knowledge building. International Journal of Computer-Supported Collaborative Learning, 1(1), 57–87.

    Article  Google Scholar 

  • Lin, X., & Lehman, J. D. (1999). Supporting learning of variable control in a computer-based biology environment. Journal of Research in Science Teaching, 36, 837–858.

    Article  Google Scholar 

  • Ljung, G., & Box, G. (1979). On a measure of lack of fit in time series models. Biometrika, 66, 265–270.

    Article  Google Scholar 

  • Lu, J., Chiu, M., & Law, N. (2011). Collaborative argumentation and justifications: A statistical discourse analysis of online discussions. Computers in Human Behavior, 27, 946–955.

    Article  Google Scholar 

  • Luppicini, R. (2007). Review of computer mediated communication research for education. Instructional Science, 35(2), 141–185.

    Article  Google Scholar 

  • MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128.

    Article  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research. Review of Educational Research, 74, 525–556.

    Article  Google Scholar 

  • Reimann, P. (2009). Time is precious: Variable and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257.

    Article  Google Scholar 

  • Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. In B. Smith (Ed.), Liberal education in a knowledge society (pp. 67–98). Chicago: Open Court.

    Google Scholar 

  • Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3(3), 265–283.

    Article  Google Scholar 

  • Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online. Review of Educational Research, 76(1), 93–135.

    Article  Google Scholar 

  • Thagard, P. (1989). Explanatory coherence. Behavioral and Brain Sciences, 1989(12), 435–502.

    Article  Google Scholar 

  • Thomas, M. J. W. (2002). Learning within incoherent structures: The space of online discussion forums. Journal of Computer Assisted Learning, 18, 351–366.

    Article  Google Scholar 

  • 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(6), 445–470.

    Article  Google Scholar 

  • Woodruff, E., & Brett, C. (1999). Collaborative knowledge building: Preservice teachers and elementary students talking to learn. Language and Education, 13(4), 280–302.

    Article  Google Scholar 

  • Zhang, J., Scardamalia, M., Reeve, R., & Messina, R. (2009). Designs for collective cognitive responsibility in knowledge building communities. Journal of the Learning Sciences, 18(1), 7–44.

    Article  Google Scholar 

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Correspondence to Ming Ming Chiu .

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Appendix: Ancillary Data

Appendix: Ancillary Data

Table 6.A1 Student demographics

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Chiu, M.M., Fujita, N. (2014). Statistical Discourse Analysis of Online Discussions: Informal Cognition, Social Metacognition, and Knowledge Creation. In: Tan, S., So, H., Yeo, J. (eds) Knowledge Creation in Education. Education Innovation Series. Springer, Singapore. https://doi.org/10.1007/978-981-287-047-6_6

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