Using Connectionist Models to Evaluate Examinees' Response Patterns on
Using Connectionist Models to Evaluate Examinees' Response Patterns on
复制标题
使用联结主义模型评估考生的反应模式
DOI：
--
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
S. Hunka
中科院分区：
文献类型：
--
作者：
Mark J. Gierl;Ying Cui;S. Hunka
The purpose of this study is to describe how the attribute hierarchy method (AHM) can be applied to assessment engineering. The AHM is a psychometric method for classifying examinees’ test item responses into a set of attribute mastery patterns associated with different components in a cognitive model of task performance. Attribute probabilities, computed using a neural network, can be estimated for each examinee thereby providing specific information about the examinee’s attribute-mastery level. The pattern recognition approach described in this study relies on an explicit cognitive model to produce the expected response patterns. The expected response patterns serve as the input to the neural network. The model also yields the cognitive test specifications. These specifications identify the examinees’ attribute patterns which are used as output for the neural network. The purpose of the statistical pattern recognition analysis is to estimate the probability that an examinee possess specific attribute combinations based on their observed item response patterns. Two examples using student response data from a sample of algebra items on the SAT illustrate our pattern recognition approach.
