Dynamic computerized testlet-based test generation system by discrete PSO with partial course ontology

Tsai, Kun Hua; Wang, Tzone I.; Hsieh, Tung Cheng; Chiu, Ti Kai; Lee, Ming-Che

doi:10.1016/j.eswa.2009.05.090

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…In [8], [9], particle swarm optimization (PSO) was proposed to generate multiple test papers of the same quality. The test paper specification has 5 parameters: the expected lower and upper bounds of the total time and concept distribution, and the number of generated test papers.…”

Section: Related Workmentioning

confidence: 99%

An efficient multi-objective optimization approach for Online Test Paper Generation

Nguyen

Hui

Fong

2011

2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM)

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With the rapid growth of the Internet and mobile devices, Online Test Paper Generation (Online-TPG) is a promising approach for self-assessment especially in an educational environment. Online-TPG is challenging as it is a multi-objective optimization problem that is NP-hard, and it is also required to satisfy the online generation requirement. The current techniques such as dynamic programming, tabu search, swarm intelligence and biologically inspired algorithms generally require long runtime for generating good quality test papers. In this paper, we propose an efficient multi-objective optimization approach for Online-TPG. The proposed approach is based on the Constraint-based Divide-and-Conquer (DAC) technique for constraint decomposition and multi-objective optimization. In this paper, we present the proposed DAC approach for Online-TPG and its performance evaluation. The performance results have shown that the proposed approach has outperformed other TPG techniques in terms of runtime efficiency and paper quality.

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Section: Related Workmentioning

confidence: 99%

An efficient multi-objective optimization approach for Online Test Paper Generation

Nguyen

Hui

Fong

2011

2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MDCM)

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“…Although there is other research on special-case single test paper generation (1-TPG) [4], [5], [6], [16], this section focuses on work related to k-TPG.…”

Section: Related Work a Parallel Test Paper Generationmentioning

confidence: 99%

Submodular Memetic Approximation for Multiobjective Parallel Test Paper Generation

Nguyen

Hui

Fong

2017

IEEE Trans. Cybern.

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Abstract-Parallel Test Paper Generation is a biobjective distributed resource optimization problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified assessment criteria. Generating highquality parallel test papers is challenging due to its NP-hardness in both of the collective objective functions. In this paper, we propose a Submodular Memetic Approximation algorithm for solving this problem. The proposed algorithm is an adaptive memetic algorithm, which exploits the submodular property of the collective objective functions to design greedy-based approximation algorithms for enhancing steps of the multiobjective memetic algorithm. Synergizing the intensification of submodular local search mechanism with the diversification of the populationbased submodular crossover operator, our algorithm can jointly optimize the total quality maximization objective and the fairness quality maximization objective. Our memetic algorithm can achieve provable near-optimal solutions in a huge search space of large datasets in efficient polynomial runtime. Performance results on various datasets have shown that our algorithm has drastically outperformed the current techniques in terms of paper quality and runtime efficiency.

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“…The algorithms which selecting items to construct final test sheet play an important role in the high quality testing [3,4].A lot of experts and researchers concentrate on proposing test sheet generation algorithms with high efficiency. Some test assembling systems use random or manual strategy [5,6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Personalized Genetic Algorithm Incorporated Item Distribution for Test Sheet Assembling

Gu¹,

Niu²,

Chen³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:In recent years, computer-based testing has become an effective approach to evaluate students' learning level. In our previous work, a personalized genetic algorithm (PGA) for test sheet assembling was proposed. In this paper, an improved personalized genetic algorithm named PGAC which makes an improvement in the crossover process of PGA is presented. Considering item distribution, an improved algorithm incorporated item distribution (IGAID) based on PGAC is presented to assemble simulation test sheets which have good item distribution in knowledge hierarchy for each student. Experiments and comparison with random assembling algorithm and GA are conducted. The results show that PGAC supports effectively assembling a test sheet with more non-mastered items for different students, and IGAID is capable of effectively constructing simulation test sheets with good item distribution in knowledge hierarchy for each student.

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Dynamic computerized testlet-based test generation system by discrete PSO with partial course ontology

Cited by 20 publications

References 15 publications

An efficient multi-objective optimization approach for Online Test Paper Generation

An efficient multi-objective optimization approach for Online Test Paper Generation

Submodular Memetic Approximation for Multiobjective Parallel Test Paper Generation

An Improved Personalized Genetic Algorithm Incorporated Item Distribution for Test Sheet Assembling

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