AI Summary & Key Takeaways
AI InsightThe books, software and future work for ca-MST are also discussed.
📌 3 Key Takeaways
Abstract
The purpose of many test in the educational and psychological measurement is to measure test takers’ latent trait scores from responses given to a set of items. Over the years, this has been done by traditional methods (paper and pencil tests). However, compared to other test administration models (e.g., adaptive testing), traditional methods are extensively criticized in terms of producing low measurement accuracy and long test length. Adaptive testing has been proposed to overcome these problems. There are two popular adaptive testing approaches. These are computerized adaptive testing (CAT) and computer adaptive multistage testing (ca-MST). The former is a well-known approach that has been predominantly used in this field. We believe that researchers and practitioners are fairly familiar with many aspects of CAT because it has more than a hundred years of history. However, the same thing is not true for the latter one. Since ca-MST is relatively new, many researchers are not familiar with features of it. The purpose of this study is to closely examine the characteristics of ca-MST, including its working principle, the adaptation procedure called the routing method, test assembly, and scoring, and provide an overview to researchers, with the aim of drawing researchers’ attention to ca-MST and encouraging them to contribute to the research in this area. The books, software and future work for ca-MST are also discussed.