Data Report on School Dropout in a Brazilian State

School dropout is a multifaceted and persistent challenge with serious implications for public health, economic development, and social equity. In Brazil, where educational disparities remain deeply rooted in structural and contextual inequalities, addressing dropout requires more than tracking attendance, academic performance, or behavior. This study presents the development and implementation of a Relational Model (RM) aimed at identifying and mitigating school dropout risks through a comprehensive, data-informed approach. Central to this model is the Alternative Instrument for Assessing School Dropout Risk Factors (IAFREE-A), which evaluates 13 interrelated risk factors across five key dimensions: Student-School, Student-School Professionals, Student-Family, Student-Community, and Student-Student. These dimensions allow for a nuanced understanding of how relational dynamics and contextual conditions shape students’ engagement with schooling. A pilot study was carried out in the state of Mato Grosso, involving 624 students across 10 public schools. Data collection was conducted through physical questionnaires, digitized using Optical Character Recognition (OCR) technology, and processed via an integrated digital platform. This platform generates individual and institutional dropout risk profiles by mapping protective and risk factors, providing actionable insights for educators, school administrators, and policymakers. Results highlighted critical concerns such as inadequate school infrastructure, perceptions of insecurity, and weak family-school connections, reinforcing the need for early, context-specific interventions. The findings demonstrate that school dropout is not an isolated event, but the outcome of complex relational, institutional, and structural factors. The RM and IAFREE-A offer an innovative and culturally sensitive framework for prevention, enabling tailored interventions that respond to students’ lived realities. By fostering stronger connections among students, educators, families, and communities, this approach has the potential to reduce dropout rates and promote more equitable and supportive educational environments in Brazil and beyond.

Authors

Institutions

Publication Details

Journal
Conference on Digital Government Research
Published
2025-06-30
DOI
https://doi.org/10.59490/dgo.2025.1053
Primary Topic
Poverty, Education, and Child Welfare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data Report on School Dropout in a Brazilian State

Luan Filipy Freire Torres, Leogildo Alves Freires, Thiago Cordeiro, Heitor Marinho da Silva Araújo et al.
1 links
Conference on Digital Government Research
Poverty, Education, and Child Welfare
article

Data Report on School Dropout in a Brazilian State

Luan Filipy Freire Torres, Leogildo Alves Freires, Thiago Cordeiro, Heitor Marinho da Silva Araújo, Gabriel Fortes
article en

Abstract

School dropout is a multifaceted and persistent challenge with serious implications for public health, economic development, and social equity. In Brazil, where educational disparities remain deeply rooted in structural and contextual inequalities, addressing dropout requires more than tracking attendance, academic performance, or behavior. This study presents the development and implementation of a Relational Model (RM) aimed at identifying and mitigating school dropout risks through a comprehensive, data-informed approach. Central to this model is the Alternative Instrument for Assessing School Dropout Risk Factors (IAFREE-A), which evaluates 13 interrelated risk factors across five key dimensions: Student-School, Student-School Professionals, Student-Family, Student-Community, and Student-Student. These dimensions allow for a nuanced understanding of how relational dynamics and contextual conditions shape students’ engagement with schooling. A pilot study was carried out in the state of Mato Grosso, involving 624 students across 10 public schools. Data collection was conducted through physical questionnaires, digitized using Optical Character Recognition (OCR) technology, and processed via an integrated digital platform. This platform generates individual and institutional dropout risk profiles by mapping protective and risk factors, providing actionable insights for educators, school administrators, and policymakers. Results highlighted critical concerns such as inadequate school infrastructure, perceptions of insecurity, and weak family-school connections, reinforcing the need for early, context-specific interventions. The findings demonstrate that school dropout is not an isolated event, but the outcome of complex relational, institutional, and structural factors. The RM and IAFREE-A offer an innovative and culturally sensitive framework for prevention, enabling tailored interventions that respond to students’ lived realities. By fostering stronger connections among students, educators, families, and communities, this approach has the potential to reduce dropout rates and promote more equitable and supportive educational environments in Brazil and beyond.

Conference on Digital Government ResearchVol. 1
Universidade Federal da Bahia (BR), Universidade Federal de Pernambuco (BR), Alberto Hurtado University (CL), Instituto Federal de Educação, Ciência e Tecnologia de Alagoas (BR), Universidade Federal de Alagoas (BR)
Openalex Percentile: Top 85%
Poverty, Education, and Child Welfare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.