An expert system approach to improving stability and reliability of web service
Expert Systems with Applications: An International Journal
An intelligent testing system embedded with an ant colony optimization based test composition method
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Novel Online Test-Sheet Composition Approach Using Genetic Algorithm
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
An intelligent testing system embedded with an ant-colony-optimization-based test composition method
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Test-sheet composition using immune algorithm for E-learning application
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
A personalized genetic algorithm approach for test sheet assembling
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
A divide-and-conquer tabu search approach for online test paper generation
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Web-based mathematics testing with automatic assessment
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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Since the last decade, computer-assisted testing has proven to be an efficient and effective way to evaluating students' learning status such that proper tutoring strategies can be adopted to improve their learning performance. A good test will not only help the instructor evaluate the learning status of the students, but also facilitate the diagnosis of the problems embedded in the students' learning process. One of the most important and challenging issues in conducting a good test is the construction of test sheets that can meet various assessment requirements. A previous study has indicated that selecting test items to best fit multiple assessment requirements can be formulated as a mixed integer programming model. The problem is known to be NP-hard in the literature and, hence, computational challenges hinder the development of efficient solution methods. As a sequel, we instead seek quality approximate solutions in a reasonable time. Two approximation methods based upon a genetic approach are developed. Statistics from a series of computational experiments indicate that our approach is able to efficiently generate near-optimal combinations of test items that satisfy the specified requirements or constraints.