Automatic aid system to enhance the readability of scientific papers

• J. Ulisses ^[1] ; R. Díaz Redondo ^[1] ; A. Fernández Vilas ^[1]
  1. [1] Universidade de Vigo

     Universidade de Vigo

     Vigo, España

     
• Localización: EDULEARN19 Proceedings: 11th International Conference on Education and New Learning Technologies (July 1st-3rd, 2019, Palma, Spain) / coord. por Luis Gómez Chova, A. López Martínez, I. Candel Torres, 2019, ISBN 978-84-09-12031-4, págs. 2416-2424
• Idioma: inglés
• Enlaces
  • Texto Completo Libro
• Resumen

  • Everyday more scientific papers are published, and science grows. However, the amount of information required for students to keep updated seems overwhelming. From the ever-present smartphone to the new wearables, mobile devices are everywhere and taking more and more part in our lives. Our proposal is enriching the framework of these devices (smartphones, laptops or even e-book readers) to help students to keep updated in different ways to deliver scientific content in a more personal and comfortable form. Many techniques can be used in order to enhance the user interest, readability and information retention and the way and form the information should be displayed is important. Technologies such as text-to-speech may offer complimentary help or replace some parts. Videos may also help deliver the information fast, so the user is more focused in a short period of time. How to create these from the information available in scientific papers is precisely explored in this paper. What is more, personal data can be collected, such as research interests or previous readings, which can provide important information on how to adapt certain techniques to the user for a better result. An early state of this data architecture is shown. This paper aims to show current techniques being used and explore possibilities provided by mixing different techniques and strategies and put them to test with the use of technology. In order to do so, work will be focused on developing an architecture that can evaluate readers’ performance and get feedback from multiple reading scenarios. The architecture will be presented showing its different modules. Each module will be explained and how it can solve a step of the problem. The methodology used allows to replace modules as improved modules with different techniques are found through user feedback.