Gender Differences in Eye Movements During Online Reading

  • Zehui ZhanEmail author
  • Jun Wu
  • Hu Mei
  • Patrick S.W. Fong
  • Muxiong Huang
  • Fangfang Shao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1048)


This paper provides evidence of individual difference on digital reading, by examining male/female and good/poor readers’ eye tracking records (including pupil, blinks, fixations, saccades, and regressions). From 6,520 undergraduate students’ university entrance exam scores and the follow-up reading assessments, we strategically selected 74 participants (half males and half females, top 3% good readers and bottom 3% poor readers) from different disciplines. Results indicated that the major differences between male and female readers were saccade duration, regression rate, and blink rate. Good and poor readers differ significantly in most of the eye tracking indicators, and the major effects of reading ability were more obvious than the gender effects. Among all the eight indicators examined in this study, blink and regression rates are most sensitive to gender attribute, while fixation rate and saccade amplitude presented least sensitiveness. These findings would be helpful for user modeling with eye-tracking data in intelligent tutoring systems.


Gender difference Reading ability Eye movement Individual difference Eye tracking Digital reading 



This study was supported by the Natural Science Foundation in China (#61305144), the Pearl River Scholar Scheme in Guangdong Province (file No.79 in 2017), the Major basic research and applied research projects of Guangdong Education Department (#2017WZDXM004), the Youth Research project in Guangdong Province (#2017WT016); MOE (Ministry of Education in China) Project of Humanities and Social Sciences (#19YJC880125); National Social Science Foundation of China (#18BGL053), the Key Institute of Humanities and Social Sciences by the MOE (#16JJD8800025), the Science & Technology Project of Guangzhou Municipality(#201604010054). We also want to thank all the participants in this study for their cooperation.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zehui Zhan
    • 1
    Email author
  • Jun Wu
    • 2
  • Hu Mei
    • 3
  • Patrick S.W. Fong
    • 4
  • Muxiong Huang
    • 1
  • Fangfang Shao
    • 1
  1. 1.School of Information Technology in EducationSouth China Normal UniversityGuangzhouChina
  2. 2.School of PsychologySouth China Normal UniversityGuangzhouChina
  3. 3.Scientific Laboratory of Economic Behavior, School of Economics and ManagementSouth China Normal UniversityGuangzhouChina
  4. 4.Department of Building and Real EstateThe Hong Kong Polytechnic UniversityHong KongChina

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