## 3 Results and Discussion  
In this research, data were first collected through questionnaires provided to WRE students. Initial calculations were performed, and then the data were transferred to a fuzzy environment using a triangular fuzzy rule to conduct pairwise comparisons and assign weights. Following this, step-by-step calculations for TOPSIS were carried out using Excel software, and the results are presented in separate tables. Below is a detailed description of the process and the data presented in the tables. Table 2 shows a pairwise comparison of matrix criteria for the AHP method in a triangular fuzzy environment. The mean values were calculated in a defined range limited to 1 to 3.5 in the fuzzy environment. However, in a pairwise comparison using the AHP approach, the numerical values of preferences for crisps often range between 1 and 9 [30]. The rules for forming this matrix must be followed precisely; for example, the values on the matrix's main diameter must be (1, 1, 1), the fuzzy triple values of each matrix array must be reversed, and the position of these values relative to each other must be adjusted, etc. More information on the creation of this matrix may be found elsewhere [76; 77]. Table 2 also displays the relative weighted coefficients of criteria for the fuzzy-AHP approach based on the preceding steps 2 and 3. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Citeria**} & \multicolumn{1}{c}{**C1**} & \multicolumn{1}{c}{**C2**} & \multicolumn{1}{c}{**C3**} & \multicolumn{1}{c}{**C4**} & \multicolumn{1}{c}{**C5**} & \multicolumn{1}{c}{**C6**} & \multicolumn{1}{c}{**C7**} & \multicolumn{1}{c}{**C8**} & \multicolumn{1}{c}{**C9**} \\ \cline{2-10}  & \multicolumn{1}{c}{**Alims and Objectives**} & \multicolumn{1}{c}{**Content**} & \multicolumn{1}{c}{**Activities**} & \multicolumn{1}{c}{**Role**} & \multicolumn{1}{c}{**Resources**} & \multicolumn{1}{c}{**Grouping**} & \multicolumn{1}{c}{**Location**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Assessment**} \\ \hline C1 & 0(1,1,1) & (2,1,3) & (2,5,3) & (2,3,1,2) & (3,2,2,5) & (3,2,2,5) & (3,2,2,5) & (2,5,2,2) & (1,3,2,2) & (1,3,2,5,1) \\ C2 & (0(1,1,1)) & (2,1,3) &  
Table 3 depicts a decision matrix created in step 4 to be used in the fuzzy-TOPSIS approach. Initially, the questionnaire was described in crisp terms and on a scale of 1 to 9 based on nine qualitative educational characteristics. Then, it was completed by students from the four universities. After that, the data were entered into this table using statistical procedures such as averaging and bounding the crisp definitive values in a definable fuzzy range [73]. Table 3 also depicts the normalized weighted fuzzy decision matrix generated by the fuzzy-TOPSIS algorithm in step 5. Data normalization involves removing the measurement scale from the data, making it easier to compare different data points. As a result, the decision matrix becomes dimensionless. Various methods can be used for data normalization, such as linear or vector methods, but in the TOPSIS, the vector method is typically employed. Based on this approach, the results presented in this table have been derived [49; 71; 73; 74; 75]. To create a FAT problem, the relative weight coefficients of the problem's criteria generated by the fuzzy-AHP technique are combined with the fuzzy matrix values of the fuzzy-TOPSIS method via mathematical procedures. The weighted coefficients of the relevance of the criteria and the grading of the importance of universities are thus established statistically, rather than qualitatively, depending on the desired criteria. Following step 6, the ideal positive and negative solutions to the issue are derived in columns 2 and 3 of Table 4. In addition, the distance of alternatives from these ideal positive and negative values is calculated in columns 4-11 based on qualitative education criteria [30; 37; 48; 51; 56].