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Title: Your eye tells how well you comprehend
Authors: Li, J
Ngai, G 
Leong, HV 
Chan, S 
Keywords: Comprehension detection
Eye gaze
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Atlanta, Georgy, June 10-14, 2016, v. 2, p. 503-508 How to cite?
Abstract: Systems that adapt to changes in human needs automatically are useful, built upon advancements in human-computer interaction research. In this paper, we investigate the problem of how well the eye movement of a user when reading an article can predict the level of reading comprehension, which could be exploited in intelligent adaptive e-learning systems. We characterize the eye movement pattern in the form of eye gaze signal. We invite human subjects in reading articles of different difficulty levels being induced to different comprehension levels. Machine-learning techniques are applied to identify useful features to recognize when readers are experiencing difficulties in understanding their reading material. Finally, a detection model that can identify different levels of user comprehension is built. We achieve a performance improvement of over 30% above the baseline, translating over 50% reduction in detection error.
ISBN: 978-1-4673-8845-0 (electronic)
978-1-4673-8846-7 (Print on Demand(PoD))
DOI: 10.1109/COMPSAC.2016.220
Appears in Collections:Conference Paper

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