Genetic algorithm based multi-agent system applied to test generation

Meng, Anbo; Luqing, Ye; Roy, Daniel; Padilla, Pierre

doi:10.1016/j.compedu.2006.01.012

Cited by 30 publications

(11 citation statements)

References 15 publications

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“…In the field of educational evaluation, the GA is used for test-sheet composition [23][24][25][26][27][28]. The test construction problem (test construction problem or item selection problem) can be formulated as a zero-one combinatorial optimization [29].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

Kim

Lee

2015

Mathematical Problems in Engineering

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The item response data is thenm-dimensional data based on the responses made bymexaminees to the questionnaire consisting ofnitems. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

Kim

Lee

2015

Mathematical Problems in Engineering

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“…This section introduced based on genetic algorithm and process according to each individual student needs and evolution of personalized course. In general, the implementation process of the genetic algorithm can be divided into four stages: initialization, selection, crossover, mutation, For the optimization problem of curricular content, the solving process is a series of knowledge abstraction of concept for representation [6], it is optimal for learning content of the organic combination of the process. Course of evolution is as shown in Figure 1.…”

Section: The Algorithm Flowmentioning

confidence: 99%

Personalized Course Evolutionary Based on Genetic Algorithm

Han¹

2014

IJMUE

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The paper presents an evolution of personalized courses based on genetic algorithms (PCEGA). The genetic algorithm are successfully applied in the dynamic update process of the course during the whole learning process. Under this framework of this algorithm, the target user model updates dynamically, and the courses evolve during the process. It provides a good general purpose and scalable framework that addresses the personalized course generation in an online learning environment.

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“…The strength of evolutionary programming is derived from their ability to exploit, in a highly efficient manner, information about a population. This search method is modeled on natural selection by Holland in [4] and is being used to solve a variety of optimization problems.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Use of an Evolutionary Approach for Question Paper Template Generation

Paul¹,

Naik²,

Rane³

et al. 2012

2012 IEEE Fourth International Conference on Technology for Education

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This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation. By using the evolutionary computational search technique of evolutionary programming and educational taxonomies, this paper analyses and experimentally proves that the generated question paper templates are best suited for dynamic examination paper generation. This new technique outperforms traditional algorithms in terms of coverage of topics, learning domains and marks distribution in the generated question paper.

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Genetic algorithm based multi-agent system applied to test generation

Cited by 30 publications

References 15 publications

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data

Personalized Course Evolutionary Based on Genetic Algorithm

Use of an Evolutionary Approach for Question Paper Template Generation

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