An intelligent testing system embedded with an ant colony optimization based test composition method

Computer-assisted testing systems are promising in generating tests efficiently and effectively for evaluating a person´s skill. This paper develops a novel intelligent testing system for both teachers and students. Equipped with user-friendly interfaces and administrative modules, the proposed system offers the following features and advantages: 1) Self-adaptive. Item attributes in an item bank are adaptively updated to reflect students´ newest learning states. 2) Reliable. Tests with high assessment qualities are reliably generated, satisfying teachers´ multiple requirements. 3) Flexible for generating parallel tests with identical test ability, especially useful for makeup exams. For students, the system is used for exercises and self-evaluation. For teachers, the system is a good helper for generating tests with different requirements. In this paper, the self-adaptation strategy and the ant colony optimization based test composition (ACO-TC) method are firstly described. ACO, an advanced computational intelligence algorithm, is used for searching high-quality results. Then the proposed testing system is introduced. The performance of the system is analyzed for composing tests in different situations.