Abstract
The item response data is thenm-dimensional data based on the responses made bymexaminees to the questionnaire consisting ofnitems. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.
Highlights
The purpose of computer simulation is to model a certain phenomenon or incident virtually in an attempt to predict the results of a real-life situation
This study used the item parameters based on classical test theory, and item parameters based on the item response theory to measure the accuracy of the algorithm
The purpose of this study is to prove the effectiveness of the genetic algorithm (GA) in generating item response data
Summary
The purpose of computer simulation is to model a certain phenomenon or incident virtually in an attempt to predict the results of a real-life situation. It is both a cost effective and time saving method for testing “what-if ” scenarios? A simulation can be run to conduct a virtual study in order to predict results of a real-life situation and establish various mathematical models (probability distribution) for certain problems. Item response data is that which shows whether the examinees responded correctly or incorrectly to the items making up a test sheet. Item response data is used to estimate item characteristics and the ability of examinees. The methods of obtaining item difficulty and item discrimination in the classical test theory and item response theory are examined
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