Abstract
Eye gaze patterns can reveal user attention, reading fluency, corrective responding, and other reading processes, suggesting they can be used to develop automated, real-time assessments of comprehension. However, past work has focused on modeling factual comprehension, whereas we ask whether gaze patterns reflect deeper levels of comprehension where inferencing and elaboration are key. We trained linear regression and random forest models to predict the quality of users’ open-ended self-explanations (SEs) collected both during and after reading and scored on a continuous scale by human raters. Our models use theoretically grounded eye tracking features (number and duration of fixations, saccade distance, proportion of regressive and horizontal saccades, spatial dispersion of fixations, and reading time) captured from a remote, head-free eye tracker (Tobii TX300) as adult users read a long expository text (6500 words) in two studies (N = 106 and 131; 247 total). Our models: (1) demonstrated convergence with human-scored SEs (r = .322 and .354), by capturing both within-user and between-user differences in comprehension; (2) were distinct from alternate models of mind-wandering and shallow comprehension; (3) predicted multiple-choice posttests of inference-level comprehension (r = .288, .354) measured immediately after reading and after a week-long delay beyond the comparison models; and (4) generalized across new users and datasets. Such models could be embedded in digital reading interfaces to improve comprehension outcomes by delivering interventions based on users’ level of comprehension.
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Notes
Correlations were similar when restricted to concepts with human-scored SEs for Study 2 (r = 0.403; < 0.001; compared to main model z = -.832, p = .405) so we focus on all-concept predictions for Study 2 in the analyses.
For Study 1, the mean absolute error (MAE) upon shuffling was .22 for the low MAE split and .44 for the high MAE split; the corresponding values for Study 2 were .08 and .38 respectively.
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Acknowledgements
The authors thank Robert Bixler for contributions to data analysis and methodology, and Candace Peacock for providing feedback on the manuscript.
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This research was supported by the National Science Foundation (NSF) (DRL 1235958, DRL 1920510). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.
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RS did methodology, formal analysis, writing—original draft preparation, reviewing and editing. CM done methodology, data collection, writing—reviewing and editing, investigation. MC was involved in writing—original draft preparation, reviewing and editing. SD contributed to conception, supervision, project administration, funding acquisition, writing—reviewing and editing.
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Southwell, R., Mills, C., Caruso, M. et al. Gaze-based predictive models of deep reading comprehension. User Model User-Adap Inter 33, 687–725 (2023). https://doi.org/10.1007/s11257-022-09346-7
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DOI: https://doi.org/10.1007/s11257-022-09346-7