  Computerized adaptive testing (CAT) refers to a form of tests that are
personalized to every student/test taker. CAT methods adaptively select the
next most informative question/item for each student given their responses to
previous questions, effectively reducing test length. Existing CAT methods use
item response theory (IRT) models to relate student ability to their responses
to questions and static question selection algorithms designed to reduce the
ability estimation error as quickly as possible; therefore, these algorithms
cannot improve by learning from large-scale student response data. In this
paper, we propose BOBCAT, a Bilevel Optimization-Based framework for CAT to
directly learn a data-driven question selection algorithm from training data.
BOBCAT is agnostic to the underlying student response model and is
computationally efficient during the adaptive testing process. Through
extensive experiments on five real-world student response datasets, we show
that BOBCAT outperforms existing CAT methods (sometimes significantly) at
reducing test length.
