Computer-based assessment: a versatile educational tool
Computers & Education
A Study of Crossover Operators in Genetic Programming
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Ordered incremental training with genetic algorithms
International Journal of Intelligent Systems
Multi-choice versus descriptive examinations
FIE '01 Proceedings of the Frontiers in Education Conference, 2001. on 31st Annual - Volume 01
test: tools for evaluation of students' tests-a development experience
FIE '01 Proceedings of the Frontiers in Education Conference, 2001. 31st Annual - Volume 02
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In this study, heuristic optimization methods which are genetic algorithm (GA), simulated annealing (SA) and adaptive simulated annealing genetic algorithm (ASAGA) are used for selecting questions from a question bank and generating a tets. The crossover and mutation operator of standard GA can not be directly usable for generating test, since integer-coded individuals have to be used and these operators produce duplicated genoms on individuals. In order to solve this problem, a mutation operation is proposed for preventing the duplications on crossovered individuals and also directing the search randomly to the new spaces. A database containing classified test questions is created together with predefined attributes for selecting questions. A particular test can be generated automatically, without active participation of the academician. The experiments and comparative analysis show that GA with proposed mutation operator is successful as nearly 100 percent and it produces results in noteworthy computational times.