In this paper the new approach to the forecasting the results of knowledge testing, proposed earlier by authors, is extended with four classes of parametric functions, the best fitting one from which is selected to approximate item characteristic function. Mathematical model is visualized by two numerical experiments. The first experiment was performed with the purpose to show the procedure of selecting the most appropriate item characteristic function and adjusting the parameters of the model. Goodness-of-fit statistic for detecting misfit of the selected model is calculated. In the second experiment a test of 10 items is constructed for the population with latent ability having normal distribution. Probability distribution of total test result and test information function are calculated when item characteristic functions are selected from four classes of parametric functions. In the next step it is shown how test information function value could be increased by adjusting parameters of item characteristic functions to the observed population. This model could be used not only for knowledge testing but also when solving diagnostic tasks in various fields of human activities. Other advantage of this method is the reduction of resources of testing process by more precise adjustment of the model parameters and decreasing the standard error of measurement of the estimated examinee ability. In the presented example the methodology is applied for solving the problem of microclimate evaluation in office rooms.
ASJC Scopus subject areas