Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/62270
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | - |
dc.creator | Zhan, Z | - |
dc.creator | Zhang, L | - |
dc.creator | Mei, H | - |
dc.creator | Fong, PSW | - |
dc.date.accessioned | 2016-12-19T08:59:20Z | - |
dc.date.available | 2016-12-19T08:59:20Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/62270 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Zhan, Z., Zhang, L., Mei, H., & Fong, P. S. W. (2016). Online learners’ reading ability detection based on eye-tracking sensors. Sensors, 16(9), (Suppl. ), - is available athttps://dx.doi.org/10.3390/s16091457 | en_US |
dc.subject | Computational model | en_US |
dc.subject | Eye-tracking sensors | en_US |
dc.subject | Online learner | en_US |
dc.subject | Reading ability detection | en_US |
dc.title | Online learners’ reading ability detection based on eye-tracking sensors | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 16 | - |
dc.identifier.issue | 9 | - |
dc.identifier.doi | 10.3390/s16091457 | - |
dcterms.abstract | The detection of university online learners’ reading ability is generally problematic and time-consuming. Thus the eye-tracking sensors have been employed in this study, to record temporal and spatial human eye movements. Learners’ pupils, blinks, fixation, saccade, and regression are recognized as primary indicators for detecting reading abilities. A computational model is established according to the empirical eye-tracking data, and applying the multi-feature regularization machine learning mechanism based on a Low-rank Constraint. The model presents good generalization ability with an error of only 4.9% when randomly running 100 times. It has obvious advantages in saving time and improving precision, with only 20 min of testing required for prediction of an individual learner’s reading ability. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Sensors, Sept. 2016, v. 16, no. 9, p. 1-17 | - |
dcterms.isPartOf | Sensors | - |
dcterms.issued | 2016 | - |
dc.identifier.scopus | 2-s2.0-84987732333 | - |
dc.identifier.ros | 2016004676 | - |
dc.identifier.eissn | 1424-8220 | - |
dc.identifier.rosgroupid | 2016004568 | - |
dc.description.ros | 2016-2017 > Academic research: refereed > Publication in refereed journal | - |
dc.description.validate | 201804_a bcma | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhan_Online_Learners_Reading.pdf | 2.94 MB | Adobe PDF | View/Open |
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