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Finding Meaning in Log-File Data

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International Handbook of Computer-Supported Collaborative Learning

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

Abstract

This chapter will start with a characterization of log-file data and related examples and then elaborate on ensuing levels of processing, interpretation/meaning-making, and finally support for decision-making and action (“actionable insights”). According to the characteristic of log files as sequences of action descriptions, we will set our focus on what has been called “Action Analysis” as compared to “Discourse Analysis.” Following up on the characterization of input data, we will review computational techniques that support the analysis of log files. Techniques of interest include process-oriented approaches (such as process mining, sequence analysis, or sequential pattern mining) as well as approaches based on social network analysis (SNA). Such techniques will be further discussed regarding their contribution to data interpretation and meaning-making. Finally, the future direction of log-file analysis is discussed considering the development of new technologies to analyze spoken conversation and nonverbal behaviors as part of action–log data.

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Further Readings

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  • Oshima, J., Oshima, R., & Fujita, W. (2018). A mixed-methods approach to analyze shared epistemic agency in jigsaw instruction at multiple scales of temporality. Journal of Learning Analytics, 5(1), 10–24. This empirical study discusses how to mix traditional discourse analysis with a new computational approach in the CSCL field. Based on the dialogism, the authors examine student collaborative discourse from the two analytical points of view of how the meaning is jointly constructed.

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  • Suthers, D. D., Dwyer, N., Medina, R., & Vatrapu, R. (2010). A framework for conceptualizing, representing, and analyzing distributed interaction. International Journal of Computer-Supported Collaborative Learning, 5(1), 5–42. This is a good example of how theory-building in the analysis of traces from educational conversations goes together with the development of formal representations and analysis methods.

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Authors and Affiliations

  1. Research and Education Center for the Learning Sciences, Shizuoka University, Shizuoka-shi, Japan

    Jun Oshima

  2. Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Germany

    H. Ulrich Hoppe

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  1. Jun Oshima
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  2. H. Ulrich Hoppe
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Correspondence to Jun Oshima .

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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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Oshima, J., Hoppe, H.U. (2021). Finding Meaning in Log-File 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_31

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  • DOI: https://doi.org/10.1007/978-3-030-65291-3_31

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