An exploratory analysis of the latent structure of process data via action sequence autoencoders
Publisher
WILEYCitation
Tang, 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.12203Rights
© 2020 The British Psychological Society.Collection Information
This 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.Abstract
Computer 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.Note
12 month embargo; published online: 22 May 2020ISSN
0007-1102EISSN
2044-8317PubMed ID
32442346Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1111/bmsp.12203
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