Xiong Jianhua, Ding Shuliang, Luo Fen, Luo Zhaosheng
School of Psychology, Jiangxi Normal University, Nanchang, China.
School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China.
Front Psychol. 2020 Jan 23;10:3085. doi: 10.3389/fpsyg.2019.03085. eCollection 2019.
Computerized adaptive testing (CAT) is an efficient testing mode, which allows each examinee to answer appropriate items according his or her latent trait level. The implementation of CAT requires a large-scale item pool, and item pool needs to be frequently replenished with new items to ensure test validity and security. Online calibration is a technique to calibrate the parameters of new items in CAT, which seeds new items in the process of answering operational items, and estimates the parameters of new items through the response data of examinees on new items. The most popular estimation methods include one EM cycle method (OEM) and multiple EM cycle method (MEM) under dichotomous item response theory models. This paper extends OEM and MEM to the graded response model (GRM), a popular model for polytomous data with ordered categories. Two simulation studies were carried out to explore online calibration under a variety of conditions, including calibration design, initial item parameter calculation methods, calibration methods, calibration sample size and the number of categories. Results show that the calibration accuracy of new items were acceptable, and which were affected by the interaction of some factors, therefore some conclusions were given.
计算机自适应测试（CAT）是一种高效的测试模式，它允许每个考生根据其潜在特质水平回答合适的题目。CAT的实施需要一个大规模的题库，并且题库需要经常用新题目进行补充，以确保测试的有效性和安全性。在线校准是一种在CAT中校准新题参数的技术，它在考生回答操作题的过程中投放新题，并通过考生对新题的回答数据来估计新题的参数。在二分法项目反应理论模型下，最流行的估计方法包括单EM循环法（OEM）和多EM循环法（MEM）。本文将OEM和MEM扩展到等级反应模型（GRM），这是一种用于有序分类多分类数据的常用模型。进行了两项模拟研究，以探索在各种条件下的在线校准，包括校准设计、初始项目参数计算方法、校准方法、校准样本量和类别数量。结果表明，新题的校准精度是可以接受的，并且受到一些因素相互作用的影响，因此给出了一些结论。
