## 6 Defuzzification and Experimental Results  
The final step in fuzzy logic systems is to convert the fuzzy membership value into a single number or numerical value. This is done using the membership function of the output variables [17; 18]. The centroid method is most commonly used for this purpose. The Centroid Method is the defuzzification method with the most appropriate and physically appealing center of gravity. The centroid method in defuzzification has the following Formula (2):  
\[\chi*=\frac{\int\mu_{c(x)xdz}}{\mu_{c(x)dz}} \tag{2}\]  
where,  
\(\chi*\) = Defuzzified output  
\(\int\) = a mathematical integration  
\(\mu_{c(x)}\) = Aggregated Membership function  
\(x\) = output variables (1). Figure 8: A rule-based fuzzy logic system. Table 7 shows how many points each disabled university student earned on their all exams. Each student's score was fuzzified using a trapezoidal membership function. Figure 9 presents the surface showing the relationship between the 3D VLE FIS input variables. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Disabled** & **GPA** & **Knowledge** & **Participation in** & **Amount of** & **3D VLE Disabled** & **Traditional Disabled** \\
**Student** & **[0–100]** & **Knowledge** & **3D VLE** & **Attendance** & **Student Assessment** & **Student Assessment** \\  & & & **[0–20]** & **[0–100]** & **[0–100]** & **[0–100]** & **[0–100]**  
In Figure 10, the MATLAB toolbox was used to calculate the performance of students with disabilities. The Mamdani fuzzy inference system was used to assess the performance of 20 primary and middle school disabled students. The results showed that disabled students generally perform worse on average than non-disabled students, but this difference is not as large as it would be if assessed using conventional methods. The evaluation results in Table 7 show that the 3D VLE FIS that uses fuzzy rules produces a higher score than the 2D VLE FIS that uses 90% of disabled students' assessment results. This means that the 3D VLE FIS is more accurate in identifying students who are disabled. The study found that fuzzy logic creates a different evaluation from that of traditional assessments of actual worth. The average difference between the fuzzy inference system and conventional evaluations is 3.9 points. Furthermore, to calculate the difference between the fuzzy inference system and conventional evaluations we used a \(t\)-test which tells us how significant the differences between groups are. This can either be a paired or unpaired \(t\)-test depending on whether the data is paired or unpaired. The goal is to find the \(p\)-value and if the \(p\)-value is less than 0.05 then there is a significant difference between the two groups. If it is more than 0.05 then there is no significant difference between the two groups. The formula for the \(t\)-test is shown below as Equation (3). \[t=\frac{\overset{-}{x}_{1-}\overset{-}{x}_{2}}{\sqrt{\bigg{(}\frac{(N_{1}-1) S_{1+}^{2}(N_{1}-1)S_{2}^{2}}{N_{1}+N_{2}-2}\bigg{)}\bigg{(}\frac{1}{N_{1}}+ \frac{1}{N_{2}}\bigg{)}}} \tag{3}\]  
Figure 10: The result of Disabled Student Assessment with MATLAB Toolbox. The study found that since the \(t\)-test function value is 0.004, which is less than 0.05, we have sufficient evidence to say that the difference between the fuzzy inference system and conventional evaluations is statistically significant. Figure 11 shows how well the assessment results match up between using classical assessment methods and using a method that uses fuzzy logic. The classical assessment results were based on the average of the four scores from the input variables [27]. Figure 11 also shows that using a 3D virtual learning environment, like the Fuzzy Logic Inference System, can help you get better results than using a traditional assessment. Figure 12 presents how we embed the tools in a 3D object allowing the disabled students to interact with them by touch.