School evasion: A hard reality

The present work has as objective to show the profile of students who abandoned the studies in a High School, located in Sao Joao de Meriti city, municipal district of Rio de Janeiro state, by means of statistical analysis. The presented indices portray an undesirable reality with almost 20% school evasion, beyond showing that more the half of the students not standing in adequate series. Keywords: School evasion, High Schools, Educational Statistics.

💡 Research Summary

The paper presents a statistical investigation of school dropout (evasion) at a public high‑school (CIEP 169) located in São João de Meriti, Rio de Janeiro state, Brazil. Using enrollment records from the 2007 academic year, the authors identified 2 408 students enrolled in the institution, of which 1 424 were in the high‑school segment. By cross‑referencing the final results register, they found that 288 students had abandoned their studies, yielding a dropout rate of 20.2 % among high‑school students. This figure aligns closely with national estimates reported by the Brazilian Institute of Geography and Statistics (IBGE), which indicate roughly a 20 % dropout rate for adolescents aged 15‑17.

The study first describes the school’s physical infrastructure (sports court, computer lab, library, auditorium) and staff composition (three directors, two pedagogical coordinators, numerous support personnel, and 76 teachers). Despite relatively good material conditions, the dropout problem persisted, suggesting that structural resources alone are insufficient to curb evasion.

Geographically, the analysis shows that the majority of students come from the host municipality (São João de Meriti, SJM), which exhibited the highest dropout proportion (21.9 %). Neighboring municipalities—Belford Roxo (18.3 %) and Rio de Janeiro city (15.9 %)—displayed comparable rates, while Duque de Caxias (9.1 %) and Nova Iguaçu (0 %) showed lower percentages, though the small sample sizes limit statistical confidence.

When disaggregated by grade and shift, a striking pattern emerges: first‑year students accounted for 56.9 % of all dropouts, second‑year for 36.8 %, and third‑year only 6.3 %. Moreover, the night shift was responsible for 71.5 % of total dropouts, whereas the day shift contributed just 28.5 %. The authors attribute the night‑shift dominance to the dual burden of work and study, fatigue, and limited institutional support for students who must balance employment with schooling.

Gender analysis revealed a slightly higher dropout share among males, but this difference was not statistically significant because the overall student body already contains a higher proportion of males (52.8 %). The paper notes that gender‑specific factors—military conscription for boys and pregnancy for girls—can also influence individual decisions to leave school.

Age‑grade mismatch is another salient finding. Approximately 60 % of the enrolled students were attending a grade that did not correspond to their chronological age, indicating widespread repetition or prior dropout. Among the dropouts, the 18‑22 year‑old cohort represented about 45 % of cases. The distribution of ages was markedly skewed; therefore, the authors preferred the median (23 years) over the mean as a measure of central tendency. Students aged 22‑26 exhibited a 40 % dropout rate, underscoring that older adolescents are especially vulnerable.

Methodologically, the study relies on descriptive statistics, frequency tables, and simple percentage calculations. The decision to use four‑year age classes rather than statistically derived intervals reflects a pragmatic approach tailored to the local educational context. The authors argue that the median is more robust given the asymmetric age distribution, and they use it to identify the “critical age” for intervention.

In the concluding section, the authors argue that the prevailing “school for all” model fails to address the specific needs of night‑shift learners and students with age‑grade incongruence. They recommend targeted interventions such as:

1. Tailored support programs for first‑year night‑shift students (tutoring, flexible scheduling, counseling).
2. Policies to reduce age‑grade mismatch, including accelerated promotion pathways or alternative certification routes.
3. Strengthened links with the local labor market to provide vocational training and income‑support mechanisms that reduce the necessity of work during school hours.

The paper emphasizes that without such focused strategies, dropout rates will likely remain high, perpetuating educational inequality and limiting social mobility in the Baixada Fluminense region. The authors hope that their statistical portrait will inform policymakers, school administrators, and community stakeholders in designing evidence‑based actions to improve retention and overall educational quality.