工作坊內容

The focus of cognitive diagnosis (CD) is on evaluating an examinee’s strengths and weaknesses in terms of cognitive skills learned and skills that need study. Current methods for fitting CD models (CDMs) work well for large-scale assessments, where the data of hundreds or thousands of examinees are available. These CDMs are constrained latent class models and serve as the item response functions as in the Item Response Theory (IRT) to fit the data. However, the development of CD-based assessment tools that can be used in small-scale test settings, say, for monitoring the instruction and learning process at the classroom level has not kept up with the rapid pace at which research and development proceeded for large-scale assessments. The main reason is that the sample sizes of the small-scale test settings are simply too small to guarantee the reliable estimation of item parameters and examinees’ proficiency class membership. 

In this workshop, an introduction to IRT and CDMs will first be given, followed by the introduction to two nonparametric methods, the nonparametric classification (NPC) method (Chiu & Douglas, 2013) and the general nonparametric classification (GNPC) method (Chiu, Sun, & Bian, 2018). The two methods allow for assigning examinees to the correct proficiency classes with a high rate when sample sizes are at the classroom level. In the end of the workshop, a hands-on practice with the related programs using R will be provided.