Can failure be prevented? Using longitudinal data to identify at‐risk students upon entering secondary school. (3rd November 2020)

• Record Type: Journal Article
• Authors: Vinas‐Forcade, Jennifer; Mels, Cindy; Van Houtte, Mieke; Valcke, Martin; Derluyn, Ilse
• Is Part Of: British educational research journal. Volume 47:Number 1(2021)
• Page Start: 205
• Page End: 225
• Publication Date: 2020-11-03
• Subjects: at‐risk students -- early identification -- grade repetition -- prediction -- Uruguay; Education -- Research -- Great Britain -- Periodicals; Éducation -- Recherche -- Grande-Bretagne -- Périodiques; 370.7
• DOI: 10.1002/berj.3683
• Languages: English
• ISSNs: 0141-1926

Abstract

Abstract : In 2016, Uruguay started gathering longitudinal student data to improve educational trajectories by putting in place an 'early alert' system. Underlying the system is the understanding that prior schooling predicts likelihood of grade repetition and grade repetition predicts later school dropout, while close follow‐up can help prevent both repetition and dropout. We used a database of administrative registries from a national public primary school graduating cohort on their last year in primary and first year in secondary education (2015–2016, n = 36, 754). We conducted two‐level cross‐classified logistic regression analyses to assess the suitability of using features of Uruguayan students' primary school trajectories, individual, family and primary school characteristics to predict their success or failure in their first year of secondary school. All considered prior schooling factors (previous repetition experiences, achievement, behaviour and absenteeism), the student's family socio‐economic status (SES) and primary school's SES composition, as well as the location of the school in an urban or rural setting, help explain differences in chances of first‐year success or failure (grade repetition) in secondary school. While these results support the 'early alert' system's approach, predictive performance analyses are needed when using explanatory models for planning interventions with scarce resources and making decisions affecting individual students' trajectories. The importance of testing resulting models' sensitivity, as well as their false positive rates, is highlighted.