What do critical reading strategies look like? Eye-tracking and lag sequential analysis reveal attention to data and reasoning when reading conflicting information

Meng Jung Tsai, An Hsuan Wu, Ivar Bråten, Ching Yeh Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Critical reading is required for information literacy in the 21st century. However, how students read and process conflicting information regarding controversial issues is still not clear. Using eye-tracking technology, this exploratory study aimed to identify visual behavior patterns that characterized critical reading strategies when students processed conflicting information about socio-scientific issues, and to examine relationships among students' visual behavior, reading strategies, and reading task outcomes. Forty-eight undergraduate and graduate students participated and read conflicting texts about food-science issues. The conflicting texts were presented in the traditional Chinese language. Participants' eye movements were tracked during the entire reading process and their self-reported critical reading strategies, recall, and opinions on the issues were collected right after reading. Pearson's correlation analyses, lag sequential analyses, and content analyses were used to analyze the data. Results showed that participants with higher scores on self-reported critical reading strategies tended to pay more attention to reasoning information in conflicting reports and to include more judgments in their responses. Further, two significant visual behavior patterns were identified for critical reading: (1) after a pause for thinking or resting, critical readers' visual attention shifted directly back to reasoning information; and (2) critical readers' visual attention shifted between reasoning and data information. These two visual behavior patterns suggested that data inspection is important for critical reasoning about conflicting information. This study provided evidence for the applicability of eye-tracking technology in assessing critical reading strategies and relationships between eye-tracking data and self-reported critical reading strategies. Findings can contribute to the future design of adaptive learning systems and teaching of critical reading strategies.

Original languageEnglish
Article number104544
JournalComputers and Education
Publication statusPublished - 2022 Oct


  • 21st century skills
  • Applications in subject areas
  • Data science applications in education
  • Information literacy
  • Teaching/learning strategies

ASJC Scopus subject areas

  • General Computer Science
  • Education


Dive into the research topics of 'What do critical reading strategies look like? Eye-tracking and lag sequential analysis reveal attention to data and reasoning when reading conflicting information'. Together they form a unique fingerprint.

Cite this