Computerized Adaptive Testing (CAT) is a widely used, efficient test mode that adapts to the examinee's proficiency level in the test domain. CAT requires pre-trained item profiles, for CAT iteratively assesses the student real-time based on the registered items' profiles, and selects the next item to administer using candidate items' profiles. However, obtaining such item profiles is a costly process that involves gathering a large, dense item-response data, then training a diagnostic model on the collected data. In this paper, we explore the possibility of leveraging response data collected in the CAT service. We first show that this poses a unique challenge due to the inherent selection bias introduced by CAT, i.e., more proficient students will receive harder questions. Indeed, when naively training the diagnostic model using CAT response data, we observe that item profiles deviate significantly from the ground-truth. To tackle the selection bias issue, we propose the user-wise aggregate influence function method. Our intuition is to filter out users whose response data is heavily biased in an aggregate manner, as judged by how much perturbation the added data will introduce during parameter estimation. This way, we may enhance the performance of CAT while introducing minimal bias to the item profiles. We provide extensive experiments to demonstrate the superiority of our proposed method based on the three public datasets and one dataset that contains real-world CAT response data.

计算机自适应测试（CAT）是一种广泛使用且高效的测试模式，它根据被考试者在测试领域的熟练程度进行自适应。本文旨在探索利用CAT服务中收集的响应数据的可能性，并提出了一种用户聚合影响函数方法来应对选择偏差问题，通过过滤出在聚合方式下响应数据偏差严重的用户，以增强CAT的性能并最小化对项目特性的偏差。

解决计算机化适应性测验中的选择偏差：一种基于用户明智聚合影响函数的方法