Gabriel Batistuta Urbano Lopes
Papers
2
Total Citations
7
H-Index
2
About
Gabriel Batistuta Urbano Lopes is a researcher at the forefront of Educational Robotics, dedicated to making hands-on STEM learning more accessible through intelligent data systems. His primary contributions lie in developing search and ingestion tools that help educators and students locate high-quality robotics learning objects from vast online repositories. His most cited work, "Robot Finder: a learning object data search and ingestion system for Educational Robotics" (2023, 4 citations), introduces a specialized platform that curates educational content on programming, electronics, and mechanics, bridging the gap between abstract theory and concrete student projects. Building on this foundation, his follow-up study, "Robot Finder v2: Search and Ingestion of Educational Robotics Data from Youtube" (2024, 3 citations), tackles the challenge of filtering the exponential growth of online video content to surface specific, pedagogically valuable robotics materials. By addressing the difficulty of navigating platforms like YouTube, Lopes’s work directly supports the integration of robotics into classrooms, helping students of all ages develop critical science, technology, engineering, and math skills. His research is particularly impactful for educators seeking to spark curiosity and innovation through interactive, project-based learning.
Research Focus
Key Achievements
Top Papers
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