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Parallel Computing with a Bayesian Item Response Model
Motorola Mobility, Chicago, USA
Department of Computer Science, Southern Illinois University, Carbondale, USA
Department of Educational Psychology & Special Education, Southern Illinois University, Carbondale, USA
Department of Computer Science, Southern Illinois University, Carbondale, USA
- 1 Motorola Mobility, Chicago, USA
- 2 Department of Computer Science, Southern Illinois University, Carbondale, USA
- 3 Department of Educational Psychology & Special Education, Southern Illinois University, Carbondale, USA
- 4 Department of Computer Science, Southern Illinois University, Carbondale, USA
American Journal of Computational Mathematics·Volume 02 (2012)·Pages 65–71·Published 21 June 2012·DOI10.4236/ajcm.2012.22009
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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.
KeywordsGibbs SamplingHigh Performance ComputingMessage Passing InterfaceTwo-Parameter IRT Model
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