Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105710
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Li, J | en_US |
dc.creator | Ngai, G | en_US |
dc.creator | Leong, HV | en_US |
dc.creator | Chan, SCF | en_US |
dc.date.accessioned | 2024-04-15T07:36:03Z | - |
dc.date.available | 2024-04-15T07:36:03Z | - |
dc.identifier.isbn | 978-1-4673-8845-0 (Electronic) | en_US |
dc.identifier.isbn | 978-1-4673-8846-7 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105710 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication J. Li, G. Ngai, H. V. Leong and S. C. F. Chan, "Your Eye Tells How Well You Comprehend," 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Atlanta, GA, USA, 2016, pp. 503-508 is available at https://doi.org/10.1109/COMPSAC.2016.220. | en_US |
dc.subject | Comprehension detection | en_US |
dc.subject | Eye gaze | en_US |
dc.subject | Reading | en_US |
dc.title | Your eye tells how well you comprehend | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 503 | en_US |
dc.identifier.epage | 508 | en_US |
dc.identifier.volume | 2 | en_US |
dc.identifier.doi | 10.1109/COMPSAC.2016.220 | en_US |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), 10-14 June 2016, Atlanta, Georgia, v. 2, p. 503-508 | en_US |
dcterms.issued | 2016 | - |
dc.identifier.scopus | 2-s2.0-84987994831 | - |
dc.relation.conference | IEEE Annual International Computer Software and Applications Conference [COMPSAC] | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-1465 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9580616 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Li_Your_Eye_Tells.pdf | Pre-Published version | 1.35 MB | Adobe PDF | View/Open |
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