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ERIC Number: ED624054
Record Type: Non-Journal
Publication Date: 2022
Pages: 13
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Going Deep and Far: Gaze-Based Models Predict Multiple Depths of Comprehension during and One Week Following Reading
Caruso, Megan; Peacock, Candace E.; Southwell, Rosy; Zhou, Guojing; D'Mello, Sidney K.
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022)
What can eye movements reveal about reading, a complex skill ubiquitous in everyday life? Research suggests that gaze can reflect short-term comprehension for facts, but it is unknown whether it can measure long-term, deep comprehension. We tracked gaze while 147 participants read long, connected, informative texts and completed assessments of rote (factual) and inference comprehension (connecting ideas) while reading a text, after reading a text, after reading five texts, and after a seven-day delay. Gaze-based student-independent computational models predicted both immediate and long-term rote and inference comprehension with moderate accuracies. Surprisingly, the models were most accurate for comprehension assessed after reading all texts and predicted comprehension even after a week-long delay. This shows that eye movements can provide a lens into the cognitive processes underlying reading comprehension, including inference formation, and the consolidation of information into long-term memory, which has implications for intelligent student interfaces that can automatically detect and repair comprehension in real-time. [For the full proceedings, see ED623995.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: DRL1920510