A novel genetic algorithm for constructing uniform test forms of cognitive diagnostic models

Cognitive diagnostic models (CDMs) are a new class of test models developed for educational assessment. They have gained growing attention in recent years for their distinctive ability to provide detailed feedback about examinees' ability. Automatic test assembly (ATA), as in other test models, has been one of the most critical issues in the development and applications of CDMs. However, developing ATA methods for CDMs is especially challenging because no close-form expressions can measure the quality of a test form based on the items used. Although some heuristic methods have been proposed for building a single test form of CDMs, few ATA methods can construct uniform test forms of CDMs, in which each test form contains a different set of items but meets equivalent demand of test quality. In order to fill the gap, this paper proposes a novel genetic algorithm (GA) for constructing uniform test forms of CDMs. The effectiveness and efficiency of the proposed method is validated on a synthetic item pool under different conditions.