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Complexity-based generation of multi-choice tests in AQG systems

This paper presents a novel method to measure the difficulty of decisions in multi-choice test questions. The concepts are presented with characteristic fuzzy functions and the entropy-based distance of the membership functions will be used to carry the difficulty level. The presented method is tested in a test automated question generation (AQG) framework.