Sometimes, the efficiency of a class is assessed by assessing the amount of knowledge that the students have after taking this class. However, this amount depends not only on the quality of the class, but also on how prepared were the students when they started taking this class. A more adequate assessment should therefore be value-added, estimating the added value that the class brought to the students. In pedagogical practice, there are many value-added assessment models. However, most existing models have two limitations. First, they model the effect of the class as an additive factor independent on the initial knowledge. In reality, the amount of knowledge learned depends on the amount of the initial knowledge. Second, the existing models are statistical, they implicitly assume that the assessment values are objective — and are subject to random measurement errors and noises. In reality, many assessment values are subjective. Thus, fuzzy techniques provide, in our opinion, a more adequate way of processing these values. In this paper, we describe how the use of fuzzy techniques can help us overcome both limitations of the existing value-added assessments.
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