Dissertation/Thesis Abstract

A parallel implementation of Gibbs sampling algorithm for 2PNO IRT models
by Rahimi, Mona, M.S., Southern Illinois University at Carbondale, 2011, 72; 1500979
Abstract (Summary)

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.

Indexing (document details)
Advisor: Mogharreban, Namdar
Commitee: Rahimi, Shahram, Sheng, Yanyan
School: Southern Illinois University at Carbondale
Department: Computer Science
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
Publication Number: 1500979
ISBN: 978-1-124-95476-9
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy