An Intelligent Testing System Embedded with an Ant Colony Optimization Based Test Composition Method

Abstract

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.