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Writing Process Differences in Subgroups Reflected in Keystroke Logs

Author

Listed:
  • Hongwen Guo
  • Mo Zhang
  • Paul Deane
  • Randy E. Bennett

Abstract

We used an unobtrusive approach, keystroke logging, to examine students’ cognitive states during essay writing. Based on data contained in the logs, we classified writing process data into three states: text production, long pause, and editing. We used semi-Markov processes to model the sequences of writing states and compared the state transition time and probability for demographic subgroups that were matched on writing proficiency. Results suggested that the subgroups employed different processes in essay writing.

Suggested Citation

  • Hongwen Guo & Mo Zhang & Paul Deane & Randy E. Bennett, 2019. "Writing Process Differences in Subgroups Reflected in Keystroke Logs," Journal of Educational and Behavioral Statistics, , vol. 44(5), pages 571-596, October.
    • Handle: RePEc:sae:jedbes:v:44:y:2019:i:5:p:571-596
    DOI: 10.3102/1076998619856590
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    References listed on IDEAS

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    1. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    2. Jackson, Christopher, 2011. "Multi-State Models for Panel Data: The msm Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 38(i08).
    3. J. R. Lockwood & Daniel F. McCaffrey, 2016. "Matching and Weighting With Functions of Error-Prone Covariates for Causal Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1831-1839, October.
    4. Król, Agnieszka & Saint-Pierre, Philippe, 2015. "SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i06).
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