Balancing Test Accuracy and Security in Computerized Adaptive Testing
Wanyong Feng Aritra Ghosh Stephen Sireci Andrew S. Lan
Computerized adaptive testing (CAT) is a form of personalized testing that
accurately measures students' knowledge levels while reducing test length.
Bilevel optimization-based CAT (BOBCAT) is a recent framework that learns a
data-driven question selection algorithm to effectively reduce test length and
improve test accuracy. However, it suffers from high question exposure and test
overlap rates, which potentially affects test security. This paper introduces a
constrained version of BOBCAT to address these problems by changing its
optimization setup and enabling us to trade off test accuracy for question
exposure and test overlap rates. We show that C-BOBCAT is effective through
extensive experiments on two real-world adult testing datasets.