Student–Facing Assessment Analytics Dashboards Based on Rasch Measurement Theory

Logistic models within item response theory (IRT) are widely used in summative assessments in education. This is because IRT predicts the proficiency level of any student more robustly than other measurement models. This strength of IRT is also important for its use in formative assessments. In the context of assessment analytics, learner ability estimated from IRT can be used in both test-based feedback (criterion-referenced, norm-referenced, and self-referenced) and task-based feedback (person-item mapping). In this study, we aim to develop a system that a) allows learners to take tests related to the course objectives, b) calculates ability predictions based on the test results according to a one-parameter logistic model (commonly known as Rasch model), and c) includes an assessment analytics dashboard with structured feedback on learners’ ability. A design-based research has been carried out for this purpose. In the first two meso cycles of the design-based research process, a student-facing assessment analytics dashboard was designed and developed as well as a testing module. The reflection and evaluation phases of the study are ongoing. In this study, the learner dashboard is presented and the design principles are discussed.