BAYESIAN ANALYSIS OF DYNAMIC ITEM RESPONSE MODELS IN EDUCATIONAL TESTING
BAYESIAN ANALYSIS OF DYNAMIC ITEM RESPONSE MODELS IN EDUCATIONAL TESTING
复制标题
DOI：
10.1214/12-aoas608
复制
发表时间：
2013-03-01
影响因子：
1.8
通讯作者：
Burdick, Donald S.
中科院分区：
文献类型：
--
作者：
Wang, Xiaojing;Berger, James O.;Burdick, Donald S.
项目反应理论（IRT）模型在教育测量测试中得到了广泛的应用。当个体可以通过时间进行重复观察时，需要将潜在能力特征的动态结构纳入模型，以适应能力的变化。在这种情况下经常出现的其他并发症包括违反常见的假设，即测试结果是有条件的独立，给定的能力和项目难度，测试项目的难度可能是部分指定的，但受到不确定性。针对时间序列二分反应数据，提出了一类新的状态空间模型--动态项目反应模型。这些模型可以追溯性地应用于全部数据，或者在需要实时预测的情况下在线应用。通过模拟实例研究了模型，并将其应用于从MetaData，Inc.获得的大量阅读测试数据。
Item response theory (IRT) models have been widely used in educational measurement testing. When there are repeated observations available for individuals through time, a dynamic structure for the latent trait of ability needs to be incorporated into the model, to accommodate changes in ability. Other complications that often arise in such settings include a violation of the common assumption that test results are conditionally independent, given ability and item difficulty, and that test item difficulties may be partially specified, but subject to uncertainty. Focusing on time series dichotomous response data, a new class of state space models, called Dynamic Item Response (DIR) models, is proposed. The models can be applied either retrospectively to the full data or on-line, in cases where real-time prediction is needed. The models are studied through simulated examples and applied to a large collection of reading test data obtained from MetaMetrics, Inc.