Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school students (n = 232) generated self-explanations while they read a science text. Recurrence Plots were generated to show qualitative differences in students’ linguistic sequences that were later quantified by indices derived by Recurrence Quantification Analysis (RQA). To predict self-explanation quality, RQA indices, along with summative measures (i.e., number of words, mean word length, and type-token ration) and general reading ability, served as predictors in a series of regression models. Regression analyses indicated that recurrence in students’ self-explanations significantly predicted human rated self-explanation quality, even after controlling for summative measures of self-explanations, individual differences, and the text that was read (R2 = 0.68). These results demonstrate the utility of RQA in exposing and quantifying temporal structure in student’s self-explanations. Further, they imply that dynamical systems methodology can be used to uncover important processes that occur during comprehension.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th International Conference on Learning Analytics and Knowledge
Subtitle of host publicationTowards User-Centred Learning Analytics, LAK 2018
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450364003
StatePublished - Mar 7 2018
Event8th International Conference on Learning Analytics and Knowledge, LAK 2018 - Sydney, Australia
Duration: Mar 5 2018Mar 9 2018

Publication series

NameACM International Conference Proceeding Series


Other8th International Conference on Learning Analytics and Knowledge, LAK 2018


  • Dynamical systems theory
  • Reading
  • Recurrence quantification analysis
  • Self-explanation
  • Text comprehension

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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