### Abstract

The simultaneously construct IRT-based (Item Response Theory) parallel tests problem requires large numbers of variables and constraints, which leads to high computational complexities and now there is no polynomial time algorithm that exists for finding the optimal solution. This article proposes an adapted CLONALG algorithm to simultaneously construct IRT-based parallel tests. Based on the CLONALG features, the proposed scheme can use a single test construction model to simultaneously construct multiple parallel tests. At the same time, it avoids the inequality problem in the sequential construction and solves the drawback of larger numbers of variables and constraints in the simultaneous construction. The serial experiments show that the proposed scheme has a lower deviation in simultaneously constructing parallel tests than the Linear Programming (LP) and the Genetic Algorithm (GA). It is also able to construct parallel tests with identical test specifications from a large item bank.

Original language | English |
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Pages (from-to) | 979-994 |

Number of pages | 16 |

Journal | Applied Intelligence |

Volume | 36 |

Issue number | 4 |

DOIs | |

Publication status | Published - 2012 Jun 1 |

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### All Science Journal Classification (ASJC) codes

- Artificial Intelligence

### Cite this

*Applied Intelligence*,

*36*(4), 979-994. https://doi.org/10.1007/s10489-011-0308-x

}

*Applied Intelligence*, vol. 36, no. 4, pp. 979-994. https://doi.org/10.1007/s10489-011-0308-x

**Simultaneously construct IRT-based parallel tests based on an adapted CLONALG algorithm.** / Chang, Ting Yi; Shiu, You Fu.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Simultaneously construct IRT-based parallel tests based on an adapted CLONALG algorithm

AU - Chang, Ting Yi

AU - Shiu, You Fu

PY - 2012/6/1

Y1 - 2012/6/1

N2 - The simultaneously construct IRT-based (Item Response Theory) parallel tests problem requires large numbers of variables and constraints, which leads to high computational complexities and now there is no polynomial time algorithm that exists for finding the optimal solution. This article proposes an adapted CLONALG algorithm to simultaneously construct IRT-based parallel tests. Based on the CLONALG features, the proposed scheme can use a single test construction model to simultaneously construct multiple parallel tests. At the same time, it avoids the inequality problem in the sequential construction and solves the drawback of larger numbers of variables and constraints in the simultaneous construction. The serial experiments show that the proposed scheme has a lower deviation in simultaneously constructing parallel tests than the Linear Programming (LP) and the Genetic Algorithm (GA). It is also able to construct parallel tests with identical test specifications from a large item bank.

AB - The simultaneously construct IRT-based (Item Response Theory) parallel tests problem requires large numbers of variables and constraints, which leads to high computational complexities and now there is no polynomial time algorithm that exists for finding the optimal solution. This article proposes an adapted CLONALG algorithm to simultaneously construct IRT-based parallel tests. Based on the CLONALG features, the proposed scheme can use a single test construction model to simultaneously construct multiple parallel tests. At the same time, it avoids the inequality problem in the sequential construction and solves the drawback of larger numbers of variables and constraints in the simultaneous construction. The serial experiments show that the proposed scheme has a lower deviation in simultaneously constructing parallel tests than the Linear Programming (LP) and the Genetic Algorithm (GA). It is also able to construct parallel tests with identical test specifications from a large item bank.

UR - http://www.scopus.com/inward/record.url?scp=84862126344&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862126344&partnerID=8YFLogxK

U2 - 10.1007/s10489-011-0308-x

DO - 10.1007/s10489-011-0308-x

M3 - Article

AN - SCOPUS:84862126344

VL - 36

SP - 979

EP - 994

JO - Applied Intelligence

JF - Applied Intelligence

SN - 0924-669X

IS - 4

ER -