| Computerized Adaptive Test bases on Item Response Theory arose in the middle of1980's, it is an important part of the computer-based testing and modern measurementresearch, it's trait is to construct an optimal test for each examinee, this is realized byestimating the examinee's ability after administration of each item and selecting the next itembased on the current ability estimate. The final grade will be independent of examinationitems (questions). CAT can measure the real capacities of the examinees more exactly, moreimpersonality and quickly. But, it brings about a problem in psychometrics how to combineinformation quantity with content and other non-psychometrics characteristics.Firstly, the author analyzes present situation and development of CAT. Then the authorintroduces IRT-the theory basis of CAT, and briefly explains the implement method of CATsystem, and designs a framework, modules and database of a CAT system.Secondly the author analyses Max-Min Model and Weighted Deviation Model. It isfound that Max-Min Model needs a long running time and probably has no solution. Whenthe constraints are made up of question type distributing, knowledge point distributing andinformation with different dimensions, the calculated deviations of WDM cannot becompared. To deal with these problems, this paper establish a step-by-step WDM, whichsolves question of different dimensions, and stands out the target of information maximum.Finally, to deal with the prematurely and the low convergence speed of genetic algorithm,this paper improves some aspects of genetic algorithms, such as coding strategy, selectionoperation, crossover operation and mutation operation etc. then this paper designs andimplements a new algorithm of test papers generation based on adaptive genetic algorithm.The simulation shows that it could solve constraint optimization problems with goodperformance and practicability. Furthermore, the success rate and efficiency is high. |
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