Modeling item revisiting behavior in computer-based testing: Exploring the effect of item revisitations as collateral information.

Item revisiting behavior is one of the most frequently occurring test-taking strategies, and it can decrease test anxiety and improve test validity. Examinees either confirm the initial answers due to persistence of their beliefs or change to different answers after careful rethought on each part of the questions. Item revisiting sequences as collateral information reveal the examinees' underlying psychological processes, such as motivation, effort, and engagement, which supports policy makers in taking further steps to facilitate instructions for the examinees. Item revisiting behavior is commonly correlated with the latent traits of examinees, and it needs to be properly analyzed in order to make valid statistical inference. In this paper, we proposed a novel item revisiting model, in which a monotonicity assumption is considered based on the observation that examinees are more likely to revisit the current item if more revisiting behavior occurs previously. Three simulation studies were conducted: (1) to evaluate the performance of the proposed Bayesian estimation algorithm for the new model; (2) to show that ignoring item revisiting sequences induces biased parameter estimates; (3) to assess the model fit of the proposed model with the ignorable and nonignorable item revisiting behavior assumptions. The results indicate that item revisiting behavior can be effectively utilized in conjunction with responses and response times to improve parameter estimation precision. A real data example is provided to illustrate the application of the proposed model.