## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(E\) & \(I\) & \(Z\) & \(\mu_{1}\) & \(\mu_{2}\) & \(\lambda\) \\ \hline \multirow{6}{*}{\(E_{1}\)} & & \(Z_{4}\) & 0.92 & 0 & 0.498 \\  & & \(Z_{3}\) & 0.778 & 0 & 0.488 \\  & & \(Z_{6}\) & 0.991 & 0 & 0.5 \\  & & \(Z_{2}\) & 1 & 0.001 & 0.5 \\  & & \(Z_{5}\) & 0.858 & 0 & 0.495 \\  & & \(Z_{1}\) & 0.92 & 0 & 0.498 \\ \cline{2-6}  & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \cline{2-6}  & & \(Z_{6}\) & 0.998 & 0.063 & 0.532 \\  & & \(Z_{5}\) & 1 & 0.644 & 0.822 \\  & & \(Z_{4}\) & 1 & 0.605 & 0.803 \\  & & \(Z_{2}\) & 0.998 & 0.399 & 0.699 \\  & & \(Z_{1}\) & 1 & 0.574 & 0.787 \\  & & \(Z_{3}\) & 1 & 0.871 & 0.936 \\  & & \(Z_{2}\) & 0.998 & 0.063 & 0.532 \\ \hline \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \hline \multirow{6}{*}{\(E_{15}\)} & & \(Z_{5}\) & 1 & 0.722 & 0.861 \\  & & \(Z_{3}\) & 1 & 0.84 & 0.92 \\  & & \(Z_{4}\) & 1 & 0.518 & 0.759 \\  & & \(Z_{6}\) & 1 & 0.713 & 0.857 \\  & & \(Z_{1}\) & 1 & 0.664 & 0.832 \\  & & \(Z_{2}\) & 1 & 0.886 & 0.943 \\ \cline{2-6}  & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \cline{2-6}  & & \(Z_{6}\) & 0.998 & 0.316 & 0.658 \\  & & \(Z_{1}\) & 1 & 0.746 & 0.873 \\  & & \(Z_{4}\) & 1 & 0.603 & 0.802 \\  & & \(Z_{3}\) & 1 & 0.779 & 0.889 \\  & & \(Z_{2}\) & 1 & 0.85 & 0.925 \\  & & \(Z_{5}\) & 1 & 0.858 & 0.929 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fragments of the results of calculations of the first and second steps. On the third layer, the neurons of the second layer are adjusted around the importance of a given task and the repetition of the exercise. Let DM consider the performed tasks equally important, with synaptic weights \(\beta_{1}=\beta_{2}=\ldots=\beta_{6}=6\), and the synaptic weight for the repetition of the execution exercise \(\beta_{7}=10\). Calculating the postsynaptic potential of neurons of the third layer is carried out according to Formula (9). After that, on the fourth layer, the data are defuzzified to derive the levels of practical knowledge using the activation function according to Formula (10). The obtained results of the calculation of the third and fourth layers are given in Table 6.  
We obtained assessments of the levels of practical knowledge in terms of repetition of exercises. The DM analyzes the received data and can determine the final evaluation of the level of practical knowledge based on reactions in multitasking situations. For example, the final grade can be an arithmetic average. We obtained the evaluation at the \"B\" level, very good (81-90). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(E\) & \(\mathbf{Z_{1}}\) & \(\mathbf{Z_{2}}\) & \(\mathbf{Z_{3}}\) & \(\mathbf{Z_{4}}\) & \(\mathbf{Z_{5}}\) & \(\mathbf{Z_{6}}\) & \(\delta\) \\ \hline \(E_{1}\) & 0.799 & 0.838 & 0.785 & 0.732 & 0.702 & 0.57 & 1 \\ \(E_{2}\) & 0.794 & 0.871 & 0.818 & 0.812 & 0.778 & 0.645 & 0.929 \\ \(E_{3}\) & 0.833 & 0.848 & 0.819 & 0.828 & 0.811 & 0.561 & 0.857 \\ \(E_{4}\) & 0.854 & 0.901 & 0.852 & 0.853 & 0.847 & 0.774 & 0.786 \\ \(E_{5}\) & 0.847 & 0.895 & 0.876 & 0.84 & 0.864 & 0.697 & 0.714 \\ \(E_{6}\) & 0.844 & 0.903 & 0.859 & 0.845 & 0.87 & 0.828 & 0.643 \\ \(E_{7}\) & 0.857 & 0.926 & 0.909 & 0.855 & 0.883 & 0.835 & 0.571 \\ \(E_{8}\) & 0.893 & 0.924 & 0.916 & 0.861 & 0.897 & 0.844 & 0.5 \\ \(E_{9}\) & 0.853 & 0.922 & 0.894 & 0.888 & 0.888 & 0.811 & 0.429 \\ \(E_{10}\) & 0.861 & 0.918 & 0.895 & 0.87 & 0.864 & 0.816 & 0.357 \\ \(E_{11}\) & 0.868 & 0.919 & 0.911 & 0.887 & 0.88 & 0.828 & 0.286 \\ \(E_{12}\) & 0.855 & 0.899 & 0.889 & 0.87 & 0.912 & 0.827 & 0.214 \\ \(E_{13}\) & 0.862 & 0.907 & 0.909 & 0.872 & 0.89 & 0.848 & 0.143 \\ \(E_{14}\) & 0.845 & 0.898 & 0.907 & 0.888 & 0.904 & 0.82 & 0.071 \\ \(E_{15}\) & 0.848 & 0.917 & 0.893 & 0.852 & 0.884 & 0.818 & 0 \\ \hline \hline \end{tabular}  
* Next, the functions of the postsynaptic potential of neurons of the second layer are determined by Formula (8), Table 5:  
\end{table}