SIGES Alerta Temprana — El Salvador's Self-Audited School Dropout Early-Warning System
El Salvador
El Salvador's SIGES early-warning module flags students at risk of dropout (pregnancy, early union, over-age status) across 96% of the …
Honduras · Tegucigalpa · See the Honduras profile · See the Tegucigalpa profile
Evidence: Observational / pre–post Top 73% 40/100 · Ask Evidence Copilot about this practice
Honduras's national SART platform flags students at risk of dropping out — with a focus on displaced, migrant and returnee children — across all 18 departments; a 2024 fact-check found the government's own dropout-reduction claims 'not verified' due to incomplete national data.
The Sistema de Alerta y Respuesta Temprana (SART) is Honduras's national platform for identifying students at risk of dropping out, operated by the Secretaría de Educación with technical and financial support from the Norwegian Refugee Council (NRC) and the Swiss Agency for Development and Cooperation. Launched in July 2021, it draws on a data-driven dropout-prediction methodology developed through an earlier World Bank-supported research collaboration between Honduras and Guatemala.
SART assigns risk levels to students based on attendance, academic performance and vulnerability indicators - including internal displacement due to violence, disability, sexual or domestic violence, and refugee or asylum status - generating school-level alerts through a 'critical route' that principals and teachers are meant to act on. It has a specific focus on migrant and returnee children through the Ministry's National Program for Attention to Migrant and Returned Children and Adolescents.
By 2024, the Ministry reported 6,054 migrant and returnee children reinserted into the education system via SART, alongside roughly 19,000 children identified as at risk of dropping out and 9,000 flagged in irregular-migration circumstances; orientation on the system's use reached 6,869 school principals across all 18 departments.
SART lacks independent evaluation of its predictive accuracy or impact. A 2024 fact-check by Honduran outlet El Heraldo rated a government official's claimed reduction in the national dropout rate (from roughly 3% to under 1%) as 'not verified', finding that the Ministry of Education and national statistics institute lack current, complete underlying data.
National / regional programmes
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
Do you run this practice? Claim it — verified implementers get a public contact pathway and can propose corrections.
Where this practice's information was retrieved from, and when.
El Salvador
El Salvador's SIGES early-warning module flags students at risk of dropout (pregnancy, early union, over-age status) across 96% of the …
Malaysia
Malaysia's Ministry of Education (KPM) and UNICEF deploy SiPKPM, an AI early-warning system tracking 5 million learners on 7 risk …
India
UNICEF and Quest Alliance built an AI-enabled early-warning system tracking attendance and risk indicators to flag Uttar Pradesh students likely …
Guatemala
A World Bank-designed predictive model using only existing administrative data flagged Guatemalan students at risk of dropping out. A 4,000-school …
Open full copilot Grounded in cited practices — always check the sources.