Your Eye Tells How Well You Comprehend

Jiajia Li, Grace Ngai, Hong Va Leong, Stephen C.F. Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Systems that adapt to changes in human needs automatically are useful, built upon advancements in human-computer interaction research. In this paper, we investigate the problem of how well the eye movement of a user when reading an article can predict the level of reading comprehension, which could be exploited in intelligent adaptive e-learning systems. We characterize the eye movement pattern in the form of eye gaze signal. We invite human subjects in reading articles of different difficulty levels being induced to different comprehension levels. Machine-learning techniques are applied to identify useful features to recognize when readers are experiencing difficulties in understanding their reading material. Finally, a detection model that can identify different levels of user comprehension is built. We achieve a performance improvement of over 30% above the baseline, translating over 50% reduction in detection error.
Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 40th Annual Computer Software and Applications Conference Workshops, COMPSAC 2016
PublisherIEEE Computer Society
Pages503-508
Number of pages6
Volume2
ISBN (Electronic)9781467388450
DOIs
Publication statusPublished - 24 Aug 2016
Event2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016 - Atlanta, United States
Duration: 10 Jun 201614 Jun 2016

Conference

Conference2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016
CountryUnited States
CityAtlanta
Period10/06/1614/06/16

Keywords

  • comprehension detection
  • eye gaze
  • reading

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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