Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that…

As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they…

The digital transition in educational testing has introduced many new opportunities for technology to enhance large-scale assessments. These include the potential to collect and use log data on test-taker response processes routinely, and on a large scale. Process data has long been recognised as a valuable source of validation evidence in…