## Appendix D The ST-FIS Method
### Acquiring Input Variables  
According to Equation (70), the ST-FIS considers a set of three input values:  
\[Z=\{z_{1},z_{2},z_{3}\}, \tag{104}\]  
where each \(z_{k}\) for \(k=1,2,3\), represents a crisp value. As indicated around Equation (71), for each input \(z_{k}\), we must consider an associated pool of fuzzy sets generically represented by \(F\) and defined through  
\[F=\bigg{\{}B_{1}^{F},\ldots,B_{2}^{F},\ldots,B_{m(F)-1}^{F},B_{m(F)}^{F}\bigg{\}}, \tag{105}\]  
\(B_{k}^{F}\) for \(k=1,2,\ldots,m(F)\) represents a linguistic term that qualitatively interprets the input \(z_{k}\). Particularly, given a direction \(d_{i}\) for \(i=1,2,\ldots,5\), the ST-FIS takes up as input variables the fuzzy sets; \(L\) covers the learning of theoretical background, \(P\) the display of practical skills, and \(A\) the student's disposition to complete tasks. Thus, according to Equations (72)-(74) here, we associate linguistic terms typifying the fuzzy sets \(L,P\) and \(A\), explicitly:  
\[L=\Big{\{}B_{1}^{L}\rightarrow\text{Little},B_{2}^{L}\rightarrow\text{Enough},B_ {3}^{L}\rightarrow\text{Much}\Big{\}} \tag{106}\]  
\[P=\Big{\{}B_{1}^{P}\rightarrow\text{Unrealized},B_{2}^{P}\rightarrow\text{ Incomplete},B_{3}^{P}\rightarrow\text{Realized}\Big{\}} \tag{107}\]  
\[A=\Big{\{}B_{1}^{A}\rightarrow\text{Unexpected},B_{2}^{A}\rightarrow\text{ Expected}\Big{\}} \tag{108}\]  
Similarly, the \(L\), \(P\) and \(A\) input fuzzy sets ((106)-(107)) can be represented using the binary input value-membership function notation, namely,  
\[\text{L}=\Big{\{}\Big{(}z_{1},\mu_{B_{1}^{L}}(z_{1})\Big{)},\Big{(}z_{1},\mu_ {B_{2}^{L}}(z_{1})\Big{)},\Big{(}z_{1},\mu_{B_{3}^{L}}(z_{2})\Big{)}|z_{1}\in \text{U}\Big{\}}, \tag{109}\]  
\[\text{P}=\Big{\{}\Big{(}z_{2},\mu_{B_{1}^{P}}(z_{2})\Big{)},\Big{(}z_{2},\mu_ {B_{2}^{P}}(z_{2})\Big{)},\Big{(}z_{2},\mu_{B_{3}^{P}}(z_{2})\Big{)}|z_{2}\in \text{U}\Big{\}}, \tag{110}\]  
\[\text{A}=\Big{\{}\Big{(}z_{3},\mu_{B_{1}^{A}}(z_{3})\Big{)},\Big{(}z_{3},\mu_ {B_{2}^{A}}(z_{3})\Big{)}|z_{3}\in\text{U}\Big{\}}, \tag{111}\]  
where U is the universe that contains all the possible elements that belong to a particular context. For the present aims, we arrange membership functions associated with the fuzzy sets \(L\) and P that assign to each element of U a number within the interval \([0,1]\), namely,  
\[\mu_{B_{1}^{L}}(z_{1})=\left\{\begin{array}{cl}1&if&z_{1}\leq a_{L_{1}} \times\Big{(}\frac{T_{1}}{10}\Big{)}\\ \\ 1-2\Big{[}\frac{z_{1}-a_{L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)}}{\big{(}b_{ L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)}\big{)}-\big{(}a_{L_{1}}\times\big{(}\frac{T_{ 1}}{10}\big{)}\big{)}}\Big{]}^{2}&if&a_{L_{1}}\times\Big{(}\frac{T_{1}}{10} \Big{)}\leq z_{1}\leq\frac{\big{(}a_{L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)} \big{)}+\big{(}b_{L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)}\big{)}}{2}\\ \\ 2\Big{[}\frac{z_{1}-\big{(}b_{L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)}\big{)} }{\big{(}b_{L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)}\big{)}-\big{(}a_{L_{1}} \times\big{(}\frac{T_{1}}{10}\big{)}\big{)}}\Big{]}^{2}&if&\frac{\big{(}a_{L_{1}} \times\big{(}\frac{T_{1}}{10}\big{)}\big{)}+\big{(}b_{L_{1}}\times\big{(} \frac{T_{1}}{10}\big{)}\big{)}}{2}\leq z_{1}\leq\Big{(}b_{L_{1}}\times\big{(} \frac{T_{1}}{10}\big{)}\Big{)}\\ \\ 0&if&z_{1}\geq\Big{(}b_{L_{1}}\times\big{(}\frac{T_{1}}{10}\big{)}\Big{)}\\ \\ \mu_{B_{2}^{L}}(z_{1})=e^  
\[\mu_{B_{2}^{A}}(z_{3})=\left\{