Some mathematical models for deciding the number of examination questions on computerized adaptive tests

Abstract The computerized adaptive test (CAT) was developed to obtain an efficient estimate of an examinee’s ability. In traditional paper-and-pencil tests, all examinees had to answer the same number of questions. This kind of test was not fair for every examinee. Consequently, the CAT plays an important role in fair and adaptive examinations. However, the basic and necessary requirements in the CAT are the databases and computer operation rules of the examination system. This paper presents some mathematical algorithms dealing with the number of questions for the examination system. The algorithms are feasible and can determine the reasonable number of questions for a CAT system.

[1]  Theodorus Johannes Hendrikus Maria Eggen,et al.  Item Selection in Adaptive Testing with the Sequential Probability Ratio Test , 1999 .

[2]  H. Goldstein Multilevel mixed linear model analysis using iterative generalized least squares , 1986 .

[3]  C. Lewis,et al.  Using Bayesian Decision Theory to Design a Computerized Mastery Test , 1990 .

[4]  Mark D. Reckase,et al.  Comparison of SPRT and Sequential Bayes Procedures for Classifying Examinees Into Two Categories Using a Computerized Test , 1996 .

[5]  W. J. J. Veerkamp,et al.  Some New Item Selection Criteria for Adaptive Testing , 1994 .

[6]  David J. Weiss,et al.  APPLICATION OF COMPUTERIZED ADAPTIVE TESTING TO EDUCATIONAL PROBLEMS , 1984 .

[7]  Willem J. van der Linden,et al.  Bayesian item selection criteria for adaptive testing , 1998 .

[8]  Tom A. B. Snijders,et al.  Variance Component Testing in Multilevel Models , 2001 .

[9]  David J. Weiss,et al.  Improving Measurement Quality and Efficiency with Adaptive Testing , 1982 .

[10]  Hua-Hua Chang,et al.  A Global Information Approach to Computerized Adaptive Testing , 1996 .

[11]  Rob R. Meijer,et al.  Statistical Properties of the K-Index for Detecting Answer Copying. Research Report. , 2002 .

[12]  N. Longford A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects , 1987 .

[13]  M. R. Novick,et al.  Statistical Theories of Mental Test Scores. , 1971 .

[14]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[15]  Paul W. Holland,et al.  ASSESSING UNUSUAL AGREEMENT BETWEEN THE INCORRECT ANSWERS OF TWO EXAMINEES USING THE K‐INDEX: STATISTICAL THEORY AND EMPIRICAL SUPPORT , 1996 .