Multilayer Fuzzy Inference System for Predicting the Risk of Dropping Out of School at the High School Level

Abstract

This study examines high school student dropout and proposes a support tool that utilizes a neuro-fuzzy system to mitigate this issue. The system analyzes a student’s economic and social information through a human-machine interface, registering data to evaluate dropout risk levels. It is proposed as an innovative alternative and considered a development project that seeks to perform diagnostics without compromising current support mechanisms. The successful implementation of this proposal will result in tangible benefits, particularly when considering the student community in various regions of the State of Chiapas, specifically in vulnerable areas. The system yielded positive results, manifesting its stability and robustness in both design and implementation. This endeavor not only tackles the identified issue, but also functions as an efficacious and dependable mechanism for assessing and averting student attrition, thereby fortifying the education system in these locales.

Keywords

• Student desertion
• neuro-fuzzy system
• human-machine interface
• diagnosis