Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation

Zhehan Jiang; Wenchao Ma

doi:10.3389/fpsyg.2018.02142

Frontiers in Psychology (Nov 2018)

Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation

Zhehan Jiang,
Wenchao Ma

Affiliations

Zhehan Jiang: Department of University Libraries, University of Alabama, Tuscaloosa, AL, United States
Wenchao Ma: Department of Educational Studies, University of Alabama, Tuscaloosa, AL, United States

DOI: https://doi.org/10.3389/fpsyg.2018.02142
Journal volume & issue: Vol. 9

Abstract

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A log-linear cognitive diagnostic model (LCDM) is estimated via a global optimization approach- differential evolution optimization (DEoptim), which can be used when the traditional expectation maximization (EM) fails. The application of the DEoptim to LCDM estimation is introduced, explicated, and evaluated via a Monte Carlo simulation study in this article. The aim of this study is to fill the gap between the field of psychometric modeling and modern machine learning estimation techniques and provide an alternative solution in the model estimation.

Published in Frontiers in Psychology

ISSN: 1664-1078 (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Philosophy. Psychology. Religion: Psychology
Website: https://www.frontiersin.org/journals/psychology

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