Automatic Question Paper Pattern Generation using GA Approach
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Abstract
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 system. By using Genetic Algorithm (GA) and educational taxonomies, this paper analyses the initial population generation, does chromosome encoding, applies genetic manipulations and experimentally proves that the generated question paper templates are best suited for the dynamic examination paper generation system. This new approach outperforms traditional algorithms that randomly generate examination papers in terms of their topic coverage, learning domains and marks distribution.
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