Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 820190, 18 pages
Research Article

An Adaptive Test Sheet Generation Mechanism Using Genetic Algorithm

1Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan
2Department of Information and Learning Technology, National University of Tainan, 33, Section 2, Shu-Lin Street, Tainan 700, Taiwan
3Department of Information Science and Applications, Asia University, 500 Lioufeng Road, Wufeng, Taichung 41354, Taiwan

Received 7 February 2012; Revised 26 March 2012; Accepted 27 March 2012

Academic Editor: Ming Li

Copyright © 2012 Huan-Yu Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For test-sheet composition systems, it is important to adaptively compose test sheets with diverse conceptual scopes, discrimination and difficulty degrees to meet various assessment requirements during real learning situations. Computation time and item exposure rate also influence performance and item bank security. Therefore, this study proposes an Adaptive Test Sheet Generation (ATSG) mechanism, where a Candidate Item Selection Strategy adaptively determines candidate test items and conceptual granularities according to desired conceptual scopes, and an Aggregate Objective Function applies Genetic Algorithm (GA) to figure out the approximate solution of mixed integer programming problem for the test-sheet composition. Experimental results show that the ATSG mechanism can efficiently, precisely generate test sheets to meet the various assessment requirements than existing ones. Furthermore, according to experimental finding, Fractal Time Series approach can be applied to analyze the self-similarity characteristics of GA’s fitness scores for improving the quality of the test-sheet composition in the near future.