Towards mining sequences and dispersion of rhetorical moves in student written texts

Simon Knight, Roberto Martinez-Maldonado, Andrew Gibson, Simon Buckingham Shum

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


There is an increasing interest in the analysis of both student's writing and the temporal aspects of learning data. The analysis of higher-level learning features in writing contexts requires analyses of data that could be characterised in terms of the sequences and processes of textual features present. This paper (1) discusses the extant literature on sequential and process analyses of writing; and, based on this and our own first-hand experience on sequential analysis, (2) proposes a number of approaches to both pre-process and analyse sequences in whole-texts. We illustrate how the approaches could be applied to examples drawn from our own datasets of 'rhetorical moves' in written texts, and the potential each approach holds for providing insight into that data. Work is in progress to apply this model to provide empirical insights. Although, similar sequence or process mining techniques have not yet been applied to student writing, techniques applied to event data could readily be operationalised to undercover patterns in texts.

Original languageEnglish
Title of host publicationLAK’17 Conference Proceedings
EditorsInge Molenaar, Xavier Ochoa, Shane Dawson
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)9781450348706
Publication statusPublished - 2017
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2017 - Morris J Wosk Centre for Dialogue, Simon Fraser University, Vancouver, Canada
Duration: 13 Mar 201717 Mar 2017
Conference number: 7th


ConferenceInternational Learning Analytics & Knowledge Conference 2017
Abbreviated titleLAK 2017
Internet address


  • Academic writing
  • Learning analytics
  • Process mining
  • Rhetorical moves
  • Sequence mining
  • Temporal analysis
  • Text mining
  • Writing analytics

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