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 applied two different parallelism methods to the existing fully Bayesian algorithm for 2PNO IRT models so that it can be run on a high performance parallel machine with less communication load. With our parallel version of the algorithm, the empirical results show that a speedup was achieved and the execution time was considerably reduced.
|Commitee:||Rahimi, Shahram, Sheng, Yanyan|
|School:||Southern Illinois University at Carbondale|
|School Location:||United States -- Illinois|
|Source:||MAI 50/02M, Masters Abstracts International|
|Subjects:||Educational psychology, Computer science|
|Keywords:||2PNO IRT models, Gibbs sampling, Parallel computing|
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