Probabilistic Models for Computerized Adaptive Testing
This work addresses adaptive testing for educational applications, but it appears incremental as it builds on existing CAT research without demonstrating major breakthroughs.
The paper tackles the problem of Computerized Adaptive Testing (CAT) by presenting three methods, including Bayesian and neural networks as new approaches in educational testing, using data from grammar school students, but does not report specific numerical results.
In this paper we follow our previous research in the area of Computerized Adaptive Testing (CAT). We present three different methods for CAT. One of them, the item response theory, is a well established method, while the other two, Bayesian and neural networks, are new in the area of educational testing. In the first part of this paper, we present the concept of CAT and its advantages and disadvantages. We collected data from paper tests performed with grammar school students. We provide the summary of data used for our experiments in the second part. Next, we present three different model types for CAT. They are based on the item response theory, Bayesian networks, and neural networks. The general theory associated with each type is briefly explained and the utilization of these models for CAT is analyzed. Future research is outlined in the concluding part of the paper. It shows many interesting research paths that are important not only for CAT but also for other areas of artificial intelligence.