An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder
This provides a generic method for analyzing process data in educational assessments, though it is incremental as it builds on existing autoencoder techniques.
The authors tackled the problem of extracting useful information from diverse and noisy process data in computer-based assessments by proposing a sequence-to-sequence autoencoder method, which successfully compressed response processes into standard numerical vectors without requiring prior knowledge of items or interaction patterns.
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.