Going Deep and Far: Gaze-based Models Predict Multiple Depths of Comprehension During and One Week Following Reading

Autor: Megan Caruso, C, ace Peacock, Rosy Southwell, Guojing Zhou, Sidney D'Mello
Přispěvatelé: Mitrovic, Antonija, Bosch, Nigel
Rok vydání: 2022
DOI: 10.5281/zenodo.6852997
Popis: What can eye movements reveal about reading, a complex skill ubiquitous in everyday life? Research suggests that gaze can measure 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, in-formative texts and completed assessments of rote (factual) and inference (connecting ideas) comprehension while reading a text, after reading a text, after reading five texts, and after a seven-day delay. Gaze-based student-independent computa-tional models predicted both immediate and long-term rote and inference comprehension with moderate accuracies. Surprising-ly, 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 compre-hension, 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.
Databáze: OpenAIRE