Development of the Next Item Selection Procedure Using Ant Colony Optimization for Computerized Adaptive Testing

Abstract

The research was invented in order to develop the next item selection procedure using Ant Colony Optimization for computerized adaptive testing which the process is proposed by three main steps as follows the first step is dividing group of items from item bank and involved rules design before Ant Colony Optimization process, the second step is process design of Ant Colony Optimization to select the next item properly with ability level of the examinees and the third step is the format operation of computerized adaptive testing. The efficiency testing of aforementioned steps measure up the examinee ability estimate and the number of used items which operate under Monte Carlo Simulation via item bank simulation in accordance with Item Response Theory (IRT) which use Three-Parameter Logistic Model (3PL) to simulate the results from item and True ability of the examinees afterwards calculate Root Mean Square Error (RMSE) and Average Bias of examinee ability estimate compare with true ability of the examinees and Median of the number of used items in order to compare with the next item selection procedure using Maximum Information Criterion (MIC) and Hurwicz Criterion with Item Exposure Control (HC-Ex).  The results showed that the next item selection procedure using Ant Colony Optimization have  the mean of RMSE = 0.074, Average Bias = 0.001 and Median = 15. Hence the developed item selection procedure is more overall efficient than the next item selection procedure using MIC and HC-Ex.