Advances in Computerized Adaptive Testing: Modeling Response Times and Constraint Management for Skills Diagnosis

计算机化自适应测试（CAT）在高风险测试项目中越来越受欢迎。 大规模CAT的例子包括研究生入学考试、研究生管理入学考试、国家护理委员会全国理事会和武装部队职业能力倾向测试。 与纸笔考试相比，CAT的一个优点是它提供了更有效的能力估计，因为它可以根据考生的估计能力适当地调整项目选择。 这项研究解决了两个领域的CAT的至关重要的。首先，灵活的响应时间模型将开发，以帮助控制考试的持续时间，并在适当的情况下，以协助能力的测量。 第二，将开发约束管理的统计方法，以确保考试有足够的信息来诊断细粒度的技能，同时也提供准确的汇总分数。该研究的影响将是提供技术，以更好地利用响应时间信息，并提高CAT提供诊断信息的能力。 响应时间分布模型将开发，使一些假设有关的功能形式，并允许响应时间和一个潜在的特征，代表能力的研究领域之间的依赖。 估计技术将被开发，可用于以前收集的数据，从考试管理CAT。算法利用估计的响应时间分布将被构造，以更好地管理考试的持续时间，并提取信息的响应时间，以更好地估计考试的能力，旨在衡量。 除了解决响应时间问题外，还将研究管理诊断信息以评估对细粒度技能的掌握的问题，以用于也旨在提供单一汇总分数的考试。 将修改用于自适应选择项目以更有效地提供分数的CAT方法，以同时平衡技能和感兴趣属性的覆盖范围。

Computerized Adaptive Testing (CAT) has become popular in high-stakes testing programs. Examples of large-scale CATs include the Graduate Record Examination, the Graduate Management Admission Test, the National Council of State Boards Nursing, and the Armed Services Vocational Aptitude Battery. An advantage of CAT over paper-and-pencil exams is that it provides more efficient estimation of abilities because it can appropriately tailor item selection to the estimated abilities of examinees. This research addresses two areas of critical importance for CAT. First, flexible models for response times will be developed to assist in controlling the duration of an exam, and also to assist in the measurement of ability in appropriate circumstances. Second, statistical methods for constraint management will be developed to ensure that an exam has sufficient information to diagnose fine-grained skills while also providing an accurate summary score.The impact of the research will be to provide technology to better utilize response-time information and also enhance the ability of CAT to provide diagnostic information. Models for response-time distributions will be developed that make few assumptions concerning functional form and allow for dependence between response times and a latent trait that represents ability on the studied domain. Estimation techniques will be developed that may be used with data previously collected from exams administered with CAT. Algorithms for utilizing estimated response-time distributions will be constructed to better manage duration of exams and to extract information from response times to better estimate the ability the exam is designed to measure. In addition to addressing response times, the problem of managing the diagnostic information to assess mastery of fine-grained skills will be studied for exams that also aim to provide a single summary score. CAT methods for adaptively selecting items to more efficiently provide a score will be modified to simultaneously balance the coverage of skills and attributes of interest.