Parallel Computing with a Bayesian Item Response Model

Kyriakos Patsias, Mona Rahimi, Yanyan Sheng, Shahram Rahimi

Department of Computer Science, Southern Illinois University, Carbondale, USA.
Department of Educational Psychology & Special Education, Southern Illinois University, Carbondale, USA.
Motorola Mobility, Chicago, USA.

DOI: 10.4236/ajcm.2012.22009                

Abstract

Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably.

Keywords

Gibbs Sampling; High Performance Computing; Message Passing Interface; Two-Parameter IRT Model

Conflicts of Interest

The authors declare no conflicts of interest.

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