The purpose of this study was to develop predictive models of student test-taking engagement in computerized formative assessments. Using different machine learning algorithms, the models utilize student data with item responses and response time to detect aberrant test behaviors such as rapid guessing. The dataset consisted of 7,602 students…

In recent years, Automatic Item Generation (AIG) has increasingly shifted from theoretical research to operational implementation, a shift raising some unforeseen practical challenges. Specifically, generating high-quality answer choices presents several challenges such as ensuring that answer choices blend in nicely together for all possible item…

Little evidence exists regarding students' actual use of testing time in naturalistic settings, particularly in the context of state accountability assessments. This study investigates students' test completion time and performance in the context of a statewide, English Language Arts and Mathematics computer-based assessment administered in grades…

While game-based assessment offers new potential for understanding the processes students use to solve problems, it also presents new challenges in uncovering which player actions provide evidence that contributes to understanding about students' knowledge, skill, and attributes that we are interested in assessing. A development process that…