Towards extraction of subjective reading incomprehension: Analysis of eye gaze features

Ayano Okoso, Takumi Toyama, Kai Steven Kunze, Joachim Folz, Marcus Liwicki, Koichi Kise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

One way to optimize learning processes is to clearly inform the learner about problematic areas. Recent work on gaze-based CHI showed that a reader's language skill can be inferred by gaze analysis. However, only few approaches have been proposed to identify those document parts a reader finds problematic. Our goal is to develop a computational method for reading incomprehension extraction. As initial work, we analyze which eye gaze features are useful for such part-based reading incomprehension extraction at three levels of document structure: paragraphs, segments and words. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationCHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings
PublisherAssociation for Computing Machinery
Pages1325-1330
Number of pages6
Volume18
ISBN (Electronic)9781450331463
DOIs
Publication statusPublished - 2015 Apr 18
Event33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015 - Seoul, Korea, Republic of
Duration: 2015 Apr 182015 Apr 23

Other

Other33rd Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2015
CountryKorea, Republic of
CitySeoul
Period15/4/1815/4/23

Keywords

  • Eye tracking
  • Gaze features
  • Reading analysis
  • Subjective reading comprehension

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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  • Cite this

    Okoso, A., Toyama, T., Kunze, K. S., Folz, J., Liwicki, M., & Kise, K. (2015). Towards extraction of subjective reading incomprehension: Analysis of eye gaze features. In CHI 2015 - Extended Abstracts Publication of the 33rd Annual CHI Conference on Human Factors in Computing Systems: Crossings (Vol. 18, pp. 1325-1330). Association for Computing Machinery. https://doi.org/10.1145/2702613.2732896