Risk and resilience factors for primary school dropout in Côte d’Ivoire

Abstract

Risks associated with school dropout have been studied in West Africa, yet more research is needed to understand what protective factors can be associated with academic resilience (i.e., remaining in school despite facing adversity). At the beginning of our longitudinal study in rural Côte d’Ivoire, 1195 students (Mage=10.75, SDage=1.42) were enrolled in fifth grade. Two years later, 7% of students had dropped out. Characteristics related to the child (e.g., child labour), family (e.g., socioeconomic status), and school (e.g., teacher quality) were first examined distinctly. We then applied a cumulative risk (CR) framework to examine child-, family-, and school-level CR and their interactions. To understand academic resilience, we used findings from our risk analysis to identify a “high-risk enrolled” subset of children and compared them to the children who dropped out. Children who dropped out were older, involved in more child labour, had poorer literacy, owned fewer books, and attended schools with poorer learning environments. Child-level CR most strongly predicted dropout (b=-.860, OR=.424); however, children with low child-level CR were more likely to drop out when family-level CR was high (b=.227, OR=1.250). School characteristics (better infrastructure and teachers) were protective for children who were at high risk of dropout yet remained enrolled. Child-, family-, and school-level factors all contributed to dropout and these factors interact to affect dropout. Although child- and family-level factors contribute significantly to dropout, certain school factors may mitigate these risks and promote academic resilience.

Is a Phone‐Based Language and Literacy Assessment a Reliable and Valid Measure of Children's Reading Skills in Low‐Resource Settings?

“…In this sample, age does not contribute in the direction we would expect based on findings in HICs (i.e., older age is associated with higher performance), however, the findings are appropriate within this context. Despite the wide age range, all of the children within the sample are in fifth grade; those who are older are likely there due to late enrollment or grade repetition (Wortsman et al, 2023), both of which contribute to poorer academic outcomes (Jasińska & Guei, 2022; Jasińska et al, 2023; Wils, 2004). Importantly, high floor or ceiling effects were not exclusive to the phone‐based assessment, suggesting the assessment format did not contribute to overly poor or high subtest performance.…”

“…At baseline, 1195 children participated, at midline, 697 children participated, and at endline, 975 children participated. At endline, about 10% of children had dropped out of school (see Wortsman et al, 2023), moved away ( N = 43), were absent on the day of data collection ( N = 79), had died ( N = 3), or the child's status was unknown ( N = 19). 62 children were missing data at one timepoint and an additional 58 children were missing at least one measure and were further excluded from analyses.…”