An iterative approach for estimation of student performances based on linguistic evaluations

In group decision analysis, numerous approaches have been suggested in an attempt to solve the problem of aggregation of individual fuzzy opinions to form a group consensus as the basis of a group decision. In this study, an optimization model, which reflects different points of view of many decision makers by weighting fuzzy opinions, is proposed for the evaluation of student performances in student-centered learning. An iterative algorithm is provided for the solution of this model, and the consequent theorem is proved. Experimental results show that the proposed iterative algorithm yields more efficient results than do the classical optimization methods. Moreover, the WABL (weighted averaging based on the levels) method produces more accurate results than do the other frequently used defuzzification methods, such as COA (center of area) and MOM (mean of maxima).