The rapid expansion of digital and technology-enhanced assessments has enabled the capture of far more than final responses or total scores. As learners navigate traditional formats, such as multiple-choice, short-answer, and performance tasks, digital delivery platforms routinely capture response times, response revisions, navigation patterns, and item-level metadata. More advanced formats, including interactive simulations, scenario-based tasks, and game-based assessments, further record fine-grained actions such as mouse clicks, keystrokes, hint requests, sequence of operations, and decision pathways. These increasingly rich data streams provide a multidimensional view of test-taker behavior, offering evidence about cognitive processes, strategy use, persistence, and motivation that goes beyond what correctness alone can reveal. Assessment analytics refers to the systematic collection, integration, and analysis of such data generated during the assessment process. In practice, this emerging field combines principles from psychometrics, learning analytics, data science, and human-computer interaction to evaluate the quality, validity, and fairness of assessments in digital environments. The ultimate goal of assessment analytics is to produce actionable evidence about how assessments measure what they intend to measure in contemporary, technology-rich educational contexts.
Bulut et al. (Thu,) studied this question.