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The Scientific World Journal
Volume 2014, Article ID 167124, 12 pages
http://dx.doi.org/10.1155/2014/167124
Research Article

The Research on Web-Based Testing Environment Using Simulated Annealing Algorithm

1Department of Media Technology and Communication, Northeast Dianli University, Jilin, Jilin 132012, China
2College of Science, Northeast Dianli University, Jilin, Jilin 132012, China
3School of Software, Northeast Normal University, Jilin, Changchun 130117, China

Received 12 January 2014; Revised 14 April 2014; Accepted 14 April 2014; Published 14 May 2014

Academic Editor: Patricia Melin

Copyright © 2014 Peng Lu 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.

Abstract

The computerized evaluation is now one of the most important methods to diagnose learning; with the application of artificial intelligence techniques in the field of evaluation, the computerized adaptive testing gradually becomes one of the most important evaluation methods. In this test, the computer dynamic updates the learner's ability level and selects tailored items from the item pool. In order to meet the needs of the test it requires that the system has a relatively high efficiency of the implementation. To solve this problem, we proposed a novel method of web-based testing environment based on simulated annealing algorithm. In the development of the system, through a series of experiments, we compared the simulated annealing method and other methods of the efficiency and efficacy. The experimental results show that this method ensures choosing nearly optimal items from the item bank for learners, meeting a variety of assessment needs, being reliable, and having valid judgment in the ability of learners. In addition, using simulated annealing algorithm to solve the computing complexity of the system greatly improves the efficiency of select items from system and near-optimal solutions.