But Will It Make My Job Easier? Early Warning Systems Development with and for Teachers in Guatemala

• In Event: Behavior Science-Informed Dropout Early Warning Systems in El Salvador, Guatemala, and Honduras

Proposal

The USAID/Guatemala Basic Education Quality and Transitions (BEQT) Activity is advancing dropout prevention through the development and pilot testing of an early warning system in primary schools, with plans to scale up in partnership with the Ministry of Education. Building on previous dropout prevention initiatives (World Bank 2021; UNESCO 2022), BEQT has introduced an open-source mobile application designed for teachers' personal devices. This application supports data collection and provides clear, actionable insights into the three critical pillars of dropout prevention: Attendance, Behavior, and Coursework (ABC). It enables teachers to track daily attendance, monitor behavioral issues, and record coursework performance, including test results and grades. The collected data helps teachers identify students in need of additional support and informs risk committees at the school level.

Anonymized and aggregated data will be shared with municipal and ministry levels, aiming to mitigate concerns about punitive accountability. The application is designed to function offline and syncs to the cloud when a connection is available, with the added benefit of free community Wi-Fi and school-based internet connectivity enhancing teachers' access to the application and other educational resources. This infrastructure also promotes digital inclusion for students.

The tool was developed through an iterative, collaborative process involving teachers in design and beta testing phases. Incorporating behavioral science principles—such as default options to reduce data entry burden and reminders to ensure regular system use—has improved its usability and adoption. Feedback from teacher focus groups led to significant revisions, resulting in a refined tool ready for a broader testing phase across 12 municipalities. This pilot will assess the system's effectiveness in a real-world context, providing insights into its scalability and implementation. The paper discusses the development process, challenges encountered, and lessons learned, offering valuable guidance for the localization and deployment of Dropout Early Warning Systems (DEWS) in low-resource settings.

References:

Haimovich Paz,F.; Vazquez,E.J.; Adelman,M.A. (2021).Scalable Early Warning Systems for School Dropout Prevention : Evidence from a 4.000-School Randomized Controlled Trial (English). Policy Research working paper,no. WPS 9685,Impact Evaluation series Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/983591622568486300/Scalable-Early-Warning-Systems-for-School-Dropout-Prevention-Evidence-from-a-4-000-School-Randomized-Controlled-Trial

UNESCO 2022. Early warning systems for school dropout prevention in Latin America and the Caribbean. UNESCO Regional Bureau for Education in Latin American and the Caribbean. https://unesdoc.unesco.org/ark:/48223/pf0000380354_eng

Authors

• Amber K Gove, RTI International

• Cynthia Carolina del Aguila, BEQTRTI