Towards an Integrative Formative Approach of Data-Driven Decision Making, Assessment for Learning, and Diagnostic Testing

Abstract

Classification accuracy of single exams is well studied in the educational measurement literature. However, when making important decisions, such as certification decisions, one usually uses several exams: an exam set. This chapter elaborates on classification accuracy of exam sets. This is influenced by the shape of the ability distribution, the height of the standards, and the possibility for compensation. This is studied using an example from vocational education and training (VET). The classification accuracy for an exam set is computed using item response theory (IRT) simulation. Classification accuracy is high when all exams from an exam set have equal and standardized ability distributions. Furthermore, exams where few or no students pass or fail increase classification accuracy. Finally, allowing compensation increases classification accuracy.