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    An exploratory analysis of the latent structure of process data via action sequence autoencoders

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    draft3.pdf
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    Author
    Tang, Xueying
    Wang, Zhi
    Liu, Jingchen
    Ying, Zhiliang
    Affiliation
    Univ Arizona, Dept Math
    Issue Date
    2020-05-22
    Keywords
    PIAAC
    autoencoder
    log file analysis
    recurrent neural network
    response process
    
    Metadata
    Show full item record
    Publisher
    WILEY
    Citation
    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.12203
    Journal
    BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY
    Rights
    © 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 2020
    ISSN
    0007-1102
    EISSN
    2044-8317
    PubMed ID
    32442346
    DOI
    10.1111/bmsp.12203
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1111/bmsp.12203
    Scopus Count
    Collections
    UA Faculty Publications

    entitlement

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