  Computer simulations have become a popular tool of assessing complex skills
such as problem-solving skills. Log files of computer-based items record the
entire human-computer interactive processes for each respondent. The response
processes are very diverse, noisy, and of nonstandard formats. Few generic
methods have been developed for exploiting the information contained in process
data. In this article, 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-computers 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.
