In light of the widespread adoption of technology-enhanced learning and assessment platforms, there is a growing demand for innovative, high-quality, and diverse assessment questions. Automatic Question Generation (AQG) has emerged as a valuable solution, enabling educators and assessment developers to efficiently produce a large volume of test…

Automatic item generation may supply many items instantly and efficiently to assessment and learning environments. Yet, the evaluation of item quality persists to be a bottleneck for deploying generated items in learning and assessment settings. In this study, we investigated the utility of using large-language models, specifically Llama 3-8B, for…

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…