Robot Finder: a learning object data search and ingestion system for Educational Robotics

• Gabriel Batistuta Urbano Lopes UERN
• Sebastião Emidio Alves Filho UERN / UFERSA

Resumo

Educational robotics awakens students’ interest and curiosity in the world of technology and innovation. By exploring concepts in programming, electronics, and mechanics, students are introduced to subject areas allowing students of all ages to develop science, technology, engineering, and math (STEM) skills. However, getting information on this topic has become difficult since, with the growth of internet content production, learning objects have become constantly dispersed. Thus, the present work has the general objective of presenting the strategy used to search and ingest data from learning objects in an automated way in RepositORE, a repository where these objects of Education and robotics can be stored and searched by users who need to acquire certain skills. For the objects of Educational Robotics to be found more quickly and accurately, the software uses data extraction techniques to search for data from the learning objects on the web. Robot Finder optimizes the search and recording of information from internet object data so that it can be accessed by RepositORE.

Palavras-chave: Learning Objects, Educational Robotics, Data Extraction, Data Ingestion, Data Engineering