Online learners’ reading ability detection based on eye-tracking sensors

Zehui Zhan, Lei Zhang, Hu Mei, Sik Wah Fong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)


The detection of university online learners’ reading ability is generally problematic and time-consuming. Thus the eye-tracking sensors have been employed in this study, to record temporal and spatial human eye movements. Learners’ pupils, blinks, fixation, saccade, and regression are recognized as primary indicators for detecting reading abilities. A computational model is established according to the empirical eye-tracking data, and applying the multi-feature regularization machine learning mechanism based on a Low-rank Constraint. The model presents good generalization ability with an error of only 4.9% when randomly running 100 times. It has obvious advantages in saving time and improving precision, with only 20 min of testing required for prediction of an individual learner’s reading ability.
Original languageEnglish
Article number1457
JournalSensors (Switzerland)
Issue number9
Publication statusPublished - 10 Sept 2016


  • Computational model
  • Eye-tracking sensors
  • Online learner
  • Reading ability detection

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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