Motivation
Understanding interpersonal dynamics within a classroom is essential for fostering healthy school climates, detecting isolation risks early, and assembling balanced collaborative groups. Manual analysis of sociometric tests is tedious and often overlooks subtle patterns.
Technical solution
Built with Python, the application processes peer choice surveys, computes adjacency matrices, and leverages network graph algorithms to project mutual connections, group clusters, and natural leaders.
Key features
- Role mapping: Immediate identification of central peer influencers, bridges, and socially isolated students.
- Local privacy: Fully local execution ensuring student information is never dispatched to cloud services.
- Intuitive network graphs: Clean, configurable topological maps designed for teachers and pedagogical guidance teams.
Impact
Empowers educators and counselors with objective, reproducible data to guide classroom seating arrangements, anti-bullying prevention, and cooperative group design.