A Research on Genetic Algorithms Based on Test Paper Composition of Item Response Theory

Tao Sun; Yan Hua  Chu

doi:10.4028/www.scientific.net/AMR.756-759.2625

Paper Titles

Mining Closed Weighed Frequent Patterns from a Sliding Window over Data Stream
p.2606

Aspect-Oriented Programming and Modeling Based on UML
p.2610

Convergence of the Preconditioned AOR Method for an H-Matrix
p.2615

The Improved Genetic Algorithms Apply on Parameter Estimation of Two Parameters Logistic Model on Item Response Theory
p.2620

A Research on Genetic Algorithms Based on Test Paper Composition of Item Response Theory
p.2625

Comparison Results on Preconditioned AOR-Type Iterative Method for M-Matrices
p.2629

Community Evolution in Dynamic Social Networks
p.2634

Development Trend Analysis on Corn Futures Price in our Country by Multiple Regression Model
p.2639

On the First-Order Markov Model for the PLC Fading Channel
p.2644

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759A Research on Genetic Algorithms Based on Test...

A Research on Genetic Algorithms Based on Test Paper Composition of Item Response Theory

Abstract:

Along with the computer technology in the examination of the application, the examination has become a hot topic. However, test paper is an important part of the adaptive test. According to the test generation of the objectives and requirements, this paper introduces a method based on item response theory ( IRT ) thought of genetic algorithm in the test application, thereby effectively solving the problem of intelligent test paper.