A HIGH PERFORMANCE GIBBS-SAMPLING ALGORITHM FOR ITEM RESPONSE THEORY MODELS

Item response theory (IRT) is a newer and improved theory compared to the classical measurement theory. The fully Bayesian approach shows promise for IRT models. However, it is computationally expensive, and therefore is limited in various applications. It is important to seek ways to reduce the execution time and a suitable solution is the use of high performance computing (HPC). HPC offers considerably high computational power and can handle applications with high computation and memory requirements. In this work, we have modified the existing fully Bayesian algorithm for 2PNO IRT models so that it can be run on a high performance parallel machine. With this parallel version of the algorithm, the empirical results show that a speedup was achieved and the execution time was reduced considerably