Exploration of Latent Structure in Test Revision and Review Log Data

In computer-based tests allowing revision and reviews, examinees' sequence of visits and answer changes to questions can be recorded. The variable-length revision log data introduce new complexities to the collected data but, at the same time, provide additional information on examinees' test-taking behavior, which can inform test development and instructions. In the current study, we used recently proposed statistical learning methods for sequence data to provide an exploratory analysis of item-level revision and review log data. Based on the revision log data collected from computer-based classroom assessments, common prototypes of revisit and review behavior were identified. The relationship between revision behavior and various item, test, and individual covariates was further explored under a Bayesian multivariate generalized linear mixed model.