The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Tabu Search
Handbook of Approximation Algorithms and Metaheuristics (Chapman & Hall/Crc Computer & Information Science Series)
Improving Student Performance Using Self-Assessment Tests
IEEE Intelligent Systems
SIETTE: A Web-Based Tool for Adaptive Testing
International Journal of Artificial Intelligence in Education
Guest Editors' Introduction: Emerging Internet Technologies for E-Learning
IEEE Internet Computing
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
On the development of a computer-assisted testing system with genetic test sheet-generating approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A test-sheet-generating algorithm for multiple assessment requirements
IEEE Transactions on Education
A tabu search approach to generating test sheets for multiple assessment criteria
IEEE Transactions on Education
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Online Test Paper Generation (Online-TPG) is a promising approach for Web-based testing and intelligent tutoring. It generates a test paper automatically online according to user specification based on multiple assessment criteria, and the generated test paper can then be attempted over the Web by user for self-assessment. Online-TPG is challenging as it is a multi-objective optimization problem on constraint satisfaction that is NP-hard, and it is also required to satisfy the online runtime requirement. The current techniques such as dynamic programming, tabu search, swarm intelligence and biologically inspired algorithms are ineffective for Online-TPG as these techniques generally require long runtime for generating good quality test papers. In this paper, we propose an efficient approach, called DAC-TS, which is based on the principle of constraint-based divide-and-conquer (DAC) and tabu search (TS) for constraint decomposition and multi-objective optimization for Online-TPG. Our empirical performance results have shown that the proposed DAC-TS approach has outperformed other techniques in terms of runtime and paper quality.