  Computerized Adaptive Testing(CAT) refers to an online system that adaptively
selects the best-suited question for students with various abilities based on
their historical response records. Most CAT methods only focus on the quality
objective of predicting the student ability accurately, but neglect concept
diversity or question exposure control, which are important considerations in
ensuring the performance and validity of CAT. Besides, the students' response
records contain valuable relational information between questions and knowledge
concepts. The previous methods ignore this relational information, resulting in
the selection of sub-optimal test questions. To address these challenges, we
propose a Graph-Enhanced Multi-Objective method for CAT (GMOCAT). Firstly,
three objectives, namely quality, diversity and novelty, are introduced into
the Scalarized Multi-Objective Reinforcement Learning framework of CAT, which
respectively correspond to improving the prediction accuracy, increasing the
concept diversity and reducing the question exposure. We use an Actor-Critic
Recommender to select questions and optimize three objectives simultaneously by
the scalarization function. Secondly, we utilize the graph neural network to
learn relation-aware embeddings of questions and concepts. These embeddings are
able to aggregate neighborhood information in the relation graphs between
questions and concepts. We conduct experiments on three real-world educational
datasets, and show that GMOCAT not only outperforms the state-of-the-art
methods in the ability prediction, but also achieve superior performance in
improving the concept diversity and alleviating the question exposure. Our code
is available at https://github.com/justarter/GMOCAT.
