## 7 Evaluation
### Video Quality  
To evaluate the quality of the videos, we propose four questions about four different aspects, namely: (i) how an expert perceives the similarity between the videos automatically generated using our method and those created by a teacher; (ii) the perception of the experts about the visual and textual explanations generated and (iii) the perception of the experts with respect to the usefulness of the videos to help users in the process of understanding the execution of a HNN. The questions are as follows: (\(Q_{1,1}\)) _\"The automatically generated explainer video is close to or likely to the videos that could be created by a human (a teacher)\"; (\(Q_{1,2}\)) \"The automatically generated explainer video shows useful visual explanations to understand the execution of a Hopfield network\"_; (\(Q_{1,3}\)) _\"The automatically generated explainer video shows useful textual explanations to understand the execution of a Hopfield network.\"_; (\(Q_{1,4}\)) _\"The automatically generated explainer video presents the information in a way that favors the understanding of the execution of a Hopfield network\"_. The questions, answers and results are shown in Table 1 and Figure 11.  
Now, we analyze the answer given by the experts. With respect to the first question: \(Q_{1,1}\), \(44.4\%\) of the experts were strongly agree, \(44.4\%\) were agree and \(11.1\%\) were disagree.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Design and Presentation**} & \multicolumn{6}{c}{**Answers**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-7}  & **1** & & **2** & **3** & **4** & **5** & **None** \\ \hline \(Q_{4,1}\) & 0 & 1 & 0 & 6 & 2 & 0 & 3.8 \\ \(Q_{4,2}\) & 0 & 0 & 1 & 4 & 2 & 2 & 3.2 \\ \(Q_{4,3}\) & 5 & 0 & 0 & 0 & 1 & 3 & 1.1 \\ \(Q_{4,4}\) & 0 & 1 & 1 & 4 & 2 & 1 & 3.4 \\ \(Q_{4,5}\) & 0 & 0 & 1 & 5 & 3 & 0 & 4.0 \\ \(Q_{4,6}\) & 0 & 0 & 1 & 5 & 2 & 1 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design and presentation.  
Figure 11: Percentages obtained from the answers of the experts for the questions about similarity regarding human, visual/textual explanations and understanding.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Motivation**} & \multicolumn{6}{c}{**Answers**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-7}  & **1** & & **2** & **3** & **4** & **5** & **None** \\ \hline \(Q_{3,1}\) & 0 & 0 & 5 & 3 & 0 & 1 & 3.0 \\ \(Q_{3,2}\) & 0 & 4 & 1 & 1 & 2 & 1 & 2.2 \\ \(Q_{3,3}\) & 0 & 1 & 0 & 4 & 4 & 0 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Motivation.  
With respect to the second one: \(Q_{1.2}\), 33.3% of them were strongly agree, 44.4% were agree and 22.2% were disagree. With respect to the third question: \(Q_{1.3}\), 11.1% were strongly agree, 55.6% were agree, 22.2% were neutral and 11.1% were disagree. Finally, with respect to the last question: (\(Q_{1.4}\)), 44.4% were strongly agree, 44.4% were agree and 11.1% were neutral. We can conclude that our explainer videos are a useful tool to get a better understanding of the execution of a HNN, but as the average of each aspect indicate (4.2, 4.1, 3.2 and 4.3) we will need to improve them in the corresponding aspects: visual and, above all, textual explanations.