An exploratory analysis of the latent structure of process data via action sequence autoencoders
AffiliationUniv Arizona, Dept Math
MetadataShow full item record
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.12203
Rights© 2020 The British Psychological Society.
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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.
Note12 month embargo; published online: 22 May 2020
VersionFinal accepted manuscript
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