TFG: Deep learning based framework to detect and analyze student behaviours in a classroom

1 minute read

Carlota Vera has successfully presented his final degree project named “Sistema de detección y análisis de emociones y comportamientos de estudiantes en un aula docente basado en Deep learning”.

Information and Resources  
Work PDF
Tutor Roberto Calvo Palomino
Artificial intelligence is already helping in the education field to understand the needs of each student and improve the learning methodologies for a better education. Neural networks such as PoseNet or FaceMesh can help to understand the dynamic of the class without expose the student's privacy. In collaboration with Tknika a deep learning vision-based framework has been developed to detect and analyze emotions, behaviours, movements and talk-action of the students in a regular class.

This work focuses on developing a system for detecting and analyzing emotions and behaviors based on AI on a teaching environment. Main objetivo of the proyect is to provide useful information to teachers about student performance. This system detection is based on algorithms that have been designed to perform a visual analysis of images and videos using pose and face neural networks for emotion detection. After all the analysis the information obtained is adapted to use as a guide and help for teachers, who will be able to understand visually the different events that occurred during a class in relation to a certain students. Privacy has been treated as a central pillar in this project, that's why no images are saved, neither audio of the environment is analyzed.