This study introduces LIVECAT, a web-based computerized adaptive testing platform. This platform provides many functions, including writing item content, managing an item bank, creating and administering a test, reporting test results, and providing information about a test and examinees. The LIVECAT provides examination administrators with an easy and flexible environment for composing and managing examinations. It is available at http://www.thecatkorea.com/. Several tools were used to program LIVECAT, as follows: operating system, Amazon Linux; web server, nginx 1.18; WAS, Apache Tomcat 8.5; database, Amazon RDMS—Maria DB; and languages, JAVA8, HTML5/CSS, Javascript, and jQuery. The LIVECAT platform can be used to implement several item response theory (IRT) models such as the Rasch and 1-, 2-, 3-parameter logistic models. The administrator can choose a specific model of test construction in LIVECAT. Multimedia data such as images, audio files, and movies can be uploaded to items in LIVECAT. Two scoring methods (maximum likelihood estimation and expected a posteriori) are available in LIVECAT and the maximum Fisher information item selection method is applied to every IRT model in LIVECAT. The LIVECAT platform showed equal or better performance compared with a conventional test platform. The LIVECAT platform enables users without psychometric expertise to easily implement and perform computerized adaptive testing at their institutions. The most recent LIVECAT version only provides a dichotomous item response model and the basic components of CAT. Shortly, LIVECAT will include advanced functions, such as polytomous item response models, weighted likelihood estimation method, and content balancing method.

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LIVECAT 基于网络的计算机化自适应测试平台介绍

本研究介绍了 LIVECAT，一个基于网络的计算机化自适应测试平台。该平台提供了许多功能，包括编写项目内容、管理项目库、创建和管理测试、报告测试结果以及提供有关测试和考生的信息。LIVECAT 为考试管理员提供了一个简单灵活的环境来编写和管理考试。它可以在 http://www.thecatkorea.com/ 上找到。LIVECAT 的编程使用了几个工具，如下：操作系统、Amazon Linux；网络服务器，nginx 1.18；是，Apache Tomcat 8.5；数据库，Amazon RDMS——Maria DB；和语言、JAVA8、HTML5/CSS、Javascript 和 jQuery。LIVECAT 平台可用于实现多种项目响应理论 (IRT) 模型，例如 Rasch 和 1、2、3 参数逻辑模型。管理员可以在 LIVECAT 中选择特定的测试构建模型。可以将图像、音频文件和电影等多媒体数据上传到 LIVECAT 中的项目。LIVECAT 中提供了两种评分方法（最大似然估计和预期后验），最大 Fisher 信息项选择方法适用于 LIVECAT 中的每个 IRT 模型。与传统测试平台相比，LIVECAT 平台表现出同等或更好的性能。LIVECAT 平台使没有心理测量专业知识的用户能够在他们的机构轻松实施和执行计算机化的自适应测试。最新的 LIVECAT 版本只提供了一个二分项响应模型和 CAT 的基本组件。不久，LIVECAT 将包括高级功能，例如多分项响应模型、