Item Response Model Parameter Estimation Based on Bayesian Joint Likelihood Langevin MCMC Method with Open Software

Abstract

With the development of education and open software, item response model is more and more important in testing actual testees’ abilities. In item response model, testees’ abilities and item parameters are interrelated. Bayesian method is important estimation method in item response model, but calculation is difficulty. This paper use Scilab and Mysql database construct item response parameter estimation framework; provideBayesian joint likelihood with Langevin MCMC method. Compare three ways to calculate item parameters and testees’ abilities, including random walk sampling, simple Langevin sampling and target distribution Langevin sampling methods.Through the actual calculations, target distribution Langevin sampling improves the calculation speed and reduces the number of systematic sampling times.