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dc.contributor.authorTang, Xueying
dc.contributor.authorWang, Zhi
dc.contributor.authorLiu, Jingchen
dc.contributor.authorYing, Zhiliang
dc.date.accessioned2020-06-16T18:18:04Z
dc.date.available2020-06-16T18:18:04Z
dc.date.issued2020-05-22
dc.identifier.citationTang, X., Wang, Z., Liu, J. and Ying, Z. (2020), An exploratory analysis of the latent structure of process data via action sequence autoencoders. Br J Math Stat Psychol. doi:10.1111/bmsp.12203en_US
dc.identifier.issn0007-1102
dc.identifier.pmid32442346
dc.identifier.doi10.1111/bmsp.12203
dc.identifier.urihttp://hdl.handle.net/10150/641579
dc.description.abstractComputer simulations have become a popular tool for assessing complex skills such as problem-solving. Log files of computer-based items record the human-computer interactive processes for each respondent in full. The response processes are very diverse, noisy, and of non-standard formats. Few generic methods have been developed to exploit the information contained in process data. In this paper we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computer interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.en_US
dc.language.isoenen_US
dc.publisherWILEYen_US
dc.rights© 2020 The British Psychological Society.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectPIAACen_US
dc.subjectautoencoderen_US
dc.subjectlog file analysisen_US
dc.subjectrecurrent neural networken_US
dc.subjectresponse processen_US
dc.titleAn exploratory analysis of the latent structure of process data via action sequence autoencodersen_US
dc.typeArticleen_US
dc.identifier.eissn2044-8317
dc.contributor.departmentUniv Arizona, Dept Mathen_US
dc.identifier.journalBRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGYen_US
dc.description.note12 month embargo; published online: 22 May 2020en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleThe British journal of mathematical and statistical psychology
dc.source.countryEngland


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