Classic statistical evaluation models are generally absolute and therefore make it difficult to achieve reliable results. One reason for this is that the sources used, inherently, contain inaccurate information and make the conditions difficult for a valid evaluation. I
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Classic statistical evaluation models are generally absolute and therefore make it difficult to achieve reliable results. One reason for this is that the sources used, inherently, contain inaccurate information and make the conditions difficult for a valid evaluation. In this study, using fuzzy inference, educational evaluation of professors was conducted. Due to the uncertain nature of the fuzzy theory, it is possible to analyze and evaluate information more precisely. The standard questionnaire of Islamic Azad University was distributed among 105 students to evaluate teachers. Then, the priority of each survey question was determined by interviewing some professors. The weighting effect of each professor's experience in response to each question priority and, also, the number of assessors' parameter in their evaluation system were considered. Mamdani type fuzzy inference system was chosen which receives two input fuzzy and provides the desired output based on fuzzy rule base. Finally, using three methods for evaluation including classic evaluation, evaluation with the expected value and fuzzy evaluation, have shown that the rating of teachers using fuzzy logic could be closer to reality.
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