Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105481
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorWang, J-
dc.creatorFu, EY-
dc.creatorNgai, G-
dc.creatorLeong, HV-
dc.date.accessioned2024-04-15T07:34:37Z-
dc.date.available2024-04-15T07:34:37Z-
dc.identifier.issn1044-7318-
dc.identifier.urihttp://hdl.handle.net/10397/105481-
dc.language.isoenen_US
dc.publisherTaylor & Francis Inc.en_US
dc.rights© 2021 Taylor & Francis Group, LLCen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Human–Computer Interaction on 10 August 2021 (published online), available at: https://tandfonline.com/10.1080/10447318.2021.1952801.en_US
dc.titleInvestigating differences in gaze and typing behavior across writing genresen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage541-
dc.identifier.epage561-
dc.identifier.volume38-
dc.identifier.issue6-
dc.identifier.doi10.1080/10447318.2021.1952801-
dcterms.abstractWriting is one of the most common activities undertaken on a computer, and the activity of writing has been widely studied. Given that writing is an intensively cognitive process, it makes sense that the type of writing that is being produced would have an effect on the writer’s gaze and typing behaviors. However, only a few studies have explored this relationship. In this paper, we study the gaze-typing behaviors, specifically, the coordination between eye gaze and typing dynamics, of writers who are producing original articles in different genres: reminiscent, logical and creative. Our study focuses on Chinese typing, particularly via the Pinyin input method, which generates text via a two step method, and requires additional cognitive processes compared to typing in phonographic languages such as English. Our study involves 46 native Chinese speakers of varying ages from children to elderly. Our method deploys statistics- and sequence-based features to infer the mental state of the author during the writing process. The statistics-based features focus on modeling the overall gaze-typing behaviors during the process and the sequence-based features focus on the transition of the gaze-typing behaviors as the piece of writing progresses. Using a linear support-vector machine, we achieve an overall accuracy over 88% for the article-genre detection by using a leave-one-subject-out cross-validation evaluation.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of human-computer interaction, 2022, v. 38, no. 6, p. 541-561-
dcterms.isPartOfInternational journal of human-computer interaction-
dcterms.issued2022-
dc.identifier.scopus2-s2.0-85112206507-
dc.identifier.eissn1532-7590-
dc.description.validate202402 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCOMP-0132en_US
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS55101748en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Wang_Investigating_Differences_Gaze.pdfPre-Published version3.87 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Final Accepted Manuscript
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

11
Citations as of Jul 7, 2024

Downloads

5
Citations as of Jul 7, 2024

SCOPUSTM   
Citations

2
Citations as of Jul 4, 2024

WEB OF SCIENCETM
Citations

2
Citations as of Jul 4, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.