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Computer simulations have become a popular tool of assessing complex skillssuch as problem-solving skills. Log files of computer-based items record theentire human-computer interactive processes for each respondent. The responseprocesses are very diverse, noisy, and of nonstandard formats. Few genericmethods have been developed for exploiting the information contained in processdata. In this article, we propose a method to extract latent variables fromprocess data. The method utilizes a sequence-to-sequence autoencoder tocompress response processes into standard numerical vectors. It does notrequire prior knowledge of the specific items and human-computers interactionpatterns. The proposed method is applied to both simulated and real processdata to demonstrate that the resulting latent variables extract usefulinformation from the response processes.

An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder