## 8 Conclusions and Future Work  
A new framework based on data-to-text and software visualization to automatically generate explainer videos about the Hopfield neural networks' execution has been presented. A design based on experience and human-computer interface has been created and presented. A software architecture to build automatic video generation based on Computational Theory of Perceptions has been defined and explained in detail. Four modules form it:  
1. Trace (for capturing the values of the data structures in each instant of time and other relevant information generated in the process of execution).  
Frame generation/Local NLG (for creating slides containing texts and graphical representations about that).  
Global NLG/Storytelling for the incorporation of texts which allows us to obtain a global communication.  
Video generation (for generating a video in mp4 format from a set of images).  
Each module has been implemented and tested using an object-based programming language called Java (version 11). We have focused on explaining the content determination and planning phases. Technical details about the implementation have been shown, the main algorithms and data structures implemented in this work have been explained. A real application for the generation of an explainer videos from HNN's execution has been designed, implemented and explained. Finally, we have positively evaluated that they are a useful tool to obtain a better understanding of the execution of a HNN, but we will need to improve the visual and, above all, textual explanations and the narrative in future works.  
Therefore, as future work we would like to work on the following challenges:  
To improve the video by incorporating sounds, voices and more advanced avatars.  
To create an automatic style module for providing users with options about visual aspects of the video (colors, font, speed, so on).  
To investigate new methods for generating more rich sentences and narratives.  
To be able to work with large neural networks using intelligent analysis and automatic summarization of videos [62].  
To employ links in which users can directly interact with the explainer video.  
Special mention about future work is related to investigating how automatic explainer videos generation can be used as video tutorials for teaching-learning processes. The main reason is that videos online have become one of the most consumed digital resources, and millions of viewers watch them on YouTube and other platforms. For example, a tutorial on skin retouching in photoshop has been watched for more than a million times and a YouTube channel on photoshop tutorials has more than 170 thousand subscribers. In higher education, videos have also become an essential resource for students. They are integrated and employed as a fundamental part of traditional courses, and they serve as a cornerstone of many courses. On the other hand, they have become in the primary information-delivery mechanism in online classes. In fact, several experts have pointed out that e-learning is the future of education. In this regard, Global Industry Analysts projected e-learning will grow in the future [63]. Additionally, the current pandemic has driven the use of video tutorials which play an important role in the e-learning area. At the same time,