Characterising students' writing processes using temporal keystroke analysis

Donia Malekian, James Bailey, Gregor Kennedy, Paula De Barba, Sadia Nawaz

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1 Citation (Scopus)

Abstract

This work aims to characterize students' writing processes using keystroke logs and understand how the extracted characteristics influence the text quality at specific moments of writing. Earlier works have proposed predictive models characterizing students' writing processes and mainly rely on distribution-based measures of pauses obtained from the overall keystroke logs. However, the effect of isolated phases of writing has not been evaluated in these models. Moreover, current theories on writing suggest that the quality of writing depends on when specific writing behaviours are performed. This view is not examined in the keystroke logging analysis literature. Addressing the mentioned challenges, the two contributions of this work are: a) characterizing students' writing processes connected to isolated writing phases and examining their influence on writing quality; and b) temporal analysis of keystrokes and investigating whether the significance of writing characteristics varies as students progress in their writing task. Our results suggest that characterizing students' writing based on isolated writing phases is slightly more predictive of writing quality. Additionally, the effect of several writing characteristics on writing quality changes when considering the time dimension.

Original languageEnglish
Title of host publicationEDM 2019
Subtitle of host publicationProceedings of the 12th International Conference on Educational Data Mining
EditorsCollin F. Lynch, Agathe Merceron, Michel Desmarais, Roger Nkambou
Place of PublicationMontreal Canada
PublisherInternational Educational Data Mining Society
Pages354-359
Number of pages6
ISBN (Electronic)9781733673600
Publication statusPublished - 2019
Externally publishedYes
EventEducational Data Mining 2019 - Montreal , Canada
Duration: 2 Jul 20195 Jul 2019
Conference number: 12th
http://educationaldatamining.org/edm2019/

Conference

ConferenceEducational Data Mining 2019
Abbreviated titleEDM 2019
Country/TerritoryCanada
CityMontreal
Period2/07/195/07/19
Internet address

Keywords

  • Keystroke log
  • SHAP feature importance
  • Temporal analysis
  • Writing process
  • XGBoost

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