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Can failure be prevented? Using longitudinal data to identify at‐risk students upon entering secondary school

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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.
Keywords
Education, at-risk students, early identification, grade repetition, prediction, Uruguay, GRADE RETENTION, DROP-OUT, TEACHERS JUDGMENTS, EDUCATION, DETERMINANTS, REPETITION, AGE, ACHIEVEMENT, TRANSITION, ACCURACY

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MLA
Vinas-Forcade, Jennifer, et al. “Can Failure Be Prevented? Using Longitudinal Data to Identify At‐risk Students upon Entering Secondary School.” BRITISH EDUCATIONAL RESEARCH JOURNAL, vol. 47, no. 1, 2021, pp. 205–25, doi:10.1002/berj.3683.
APA
Vinas-Forcade, J., Mels, C., Van Houtte, M., Valcke, M., & Derluyn, I. (2021). Can failure be prevented? Using longitudinal data to identify at‐risk students upon entering secondary school. BRITISH EDUCATIONAL RESEARCH JOURNAL, 47(1), 205–225. https://doi.org/10.1002/berj.3683
Chicago author-date
Vinas-Forcade, Jennifer, Cindy Mels, Mieke Van Houtte, Martin Valcke, and Ilse Derluyn. 2021. “Can Failure Be Prevented? Using Longitudinal Data to Identify At‐risk Students upon Entering Secondary School.” BRITISH EDUCATIONAL RESEARCH JOURNAL 47 (1): 205–25. https://doi.org/10.1002/berj.3683.
Chicago author-date (all authors)
Vinas-Forcade, Jennifer, Cindy Mels, Mieke Van Houtte, Martin Valcke, and Ilse Derluyn. 2021. “Can Failure Be Prevented? Using Longitudinal Data to Identify At‐risk Students upon Entering Secondary School.” BRITISH EDUCATIONAL RESEARCH JOURNAL 47 (1): 205–225. doi:10.1002/berj.3683.
Vancouver
1.
Vinas-Forcade J, Mels C, Van Houtte M, Valcke M, Derluyn I. Can failure be prevented? Using longitudinal data to identify at‐risk students upon entering secondary school. BRITISH EDUCATIONAL RESEARCH JOURNAL. 2021;47(1):205–25.
IEEE
[1]
J. Vinas-Forcade, C. Mels, M. Van Houtte, M. Valcke, and I. Derluyn, “Can failure be prevented? Using longitudinal data to identify at‐risk students upon entering secondary school,” BRITISH EDUCATIONAL RESEARCH JOURNAL, vol. 47, no. 1, pp. 205–225, 2021.
@article{8688683,
  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.}},
  author       = {{Vinas-Forcade, Jennifer and Mels, Cindy and Van Houtte, Mieke and Valcke, Martin and Derluyn, Ilse}},
  issn         = {{0141-1926}},
  journal      = {{BRITISH EDUCATIONAL RESEARCH JOURNAL}},
  keywords     = {{Education,at-risk students,early identification,grade repetition,prediction,Uruguay,GRADE RETENTION,DROP-OUT,TEACHERS JUDGMENTS,EDUCATION,DETERMINANTS,REPETITION,AGE,ACHIEVEMENT,TRANSITION,ACCURACY}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{205--225}},
  title        = {{Can failure be prevented? Using longitudinal data to identify at‐risk students upon entering secondary school}},
  url          = {{http://dx.doi.org/10.1002/berj.3683}},
  volume       = {{47}},
  year         = {{2021}},
}

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