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International Handbook of Computer-Supported Collaborative Learning pp 533–550Cite as

Statistical and Stochastic Analysis of Sequence Data

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Part of the book series: Computer-Supported Collaborative Learning Series ((CULS,volume 19))

Abstract

Two common CSCL questions regarding analyses of temporal data, such as event sequences, are: (i) What variables are related to event attributes? and (ii) what is the process (or what are the processes) that generated the events? The first question is best answered with statistical methods, the second with stochastic or deterministic process modeling methods. This chapter provides an overview of statistical and stochastic methods of direct relevance to CSCL research. Many of the statistical analyses are integrated into statistical discourse analysis. From the stochastic modeling repertoire, the basic hidden Markov model as well as recent extensions is introduced, ending with dynamic Bayesian models as the current best integration. Looking into the near future, we identify opportunities for a closer alignment of qualitative with quantitative methods for temporal analysis, afforded by developments such as automization of quantitative methods and advances in computational modeling.

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Further Readings

  • Abrahamson, D., Blikstein, P., & Wilensky, U. (2007). Classroom model, model classroom: Computer-supported methodology for investigating collaborative-learning pedagogy. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the eighth International Conference on Computer Supported Collaborative Learning (CSCL) (Vol. 8, Part 1, pp. 49–58). International Society of the Learning Sciences. A powerful demonstration of how (deterministic) computational modeling can interact with empirical (classroom) research. Using the agent-based modeling tool, NetLogo, the authors provide an analysis of the mechanisms that lead to the emergence of stratified learning zones in a prototypical collaborative classroom activity. Also important because it highlights the tension between collaborative solving problems and learning from collaboration.

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  • Bergner, Y., Walker, E., & Ogan, A. (2017). Dynamic Bayesian Network models for peer tutoring interactions. In A. A. von Davier, M. Zhu, & P. C. Kyllonen (Eds.), Innovative assessment of collaboration (pp. 249–268). Springer. This chapter provides a nice illustration of the use of modern HMM approaches to analyzing (peer) tutorial dialogue. While an important area of collaborative learning, research on tutor–tutee dialogue is only partially reflected in the CSCL literature, with this chapter providing a welcome connection between CSCL, AI in Education, and assessment research. It includes an application in the context of an empirical study.

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  • 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. This empirical study applied statistical discourse analysis to test whether (a) groups that created more correct, new ideas (micro-creativity) were more likely to solve a problem and (b) students’ recent actions (microtime context of evaluations, questions, justifications, politeness, and status differences) increased subsequent micro-creativity.

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  • Chiu, M. M., & Lehmann-Willenbrock, N. (2016). Statistical discourse analysis: Modeling sequences of individual behaviors during group interactions across time. Group Dynamics: Theory, Research, and Practice, 20(3), 242–258. This article showcases statistical discourse analysis, a method that integrates most of the above methods (parallel chats, trees, group/individual differences, pivotal events, time periods, multiple target events, indirect effects, later group outcomes) and addresses related issues (e.g., missing data, inter-rater reliability, false positives, etc.).

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  • 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, 239–257. This methodological paper provides an overview of qualitative, quantitative, and computational methods for analyzing temporal data in CSCL. It argues that there is a rather fundamental difference between explaining collaboration over time in terms of variables versus explaining them in terms of events. Implications for doing temporal analysis are discussed.

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Authors and Affiliations

  1. Special Education and Counseling, The Education University of Hong Kong, Tai Po, Hong Kong

    Ming Ming Chiu

  2. Centre for Research on Learning and Innovation, University of Sydney, Sydney, Australia

    Peter Reimann

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  1. Ming Ming Chiu
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Correspondence to Ming Ming Chiu .

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Editors and Affiliations

  1. Leibniz-Institut für Wissensmedien, Tübingen, Germany

    Ulrike Cress

  2. Language Technologies Institute and Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Rosé

  3. Department of Administration Leadership, and Technology, New York University, New York, NY, USA

    Alyssa Friend Wise

  4. Faculty of Informatics, Shizuoka University, Hamamatsu-shi, Shizuoka, Japan

    Jun Oshima

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Chiu, M.M., Reimann, P. (2021). Statistical and Stochastic Analysis of Sequence Data. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds) International Handbook of Computer-Supported Collaborative Learning. Computer-Supported Collaborative Learning Series, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-65291-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-65291-3_29

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