Administrative Algorithmic Dropout Targeting

  • Finding
  • Education
  • predictive analytics
  • dropout risk
  • targeting algorithm
  • education data
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In Simple Words

Instead of waiting for students to start failing classes, administrators analyze prior middle school report cards and standardized admission test scores before the academic year begins to calculate an individual risk score. This functions like a health screening questionnaire at a hospital lobby that flags patients with elevated blood pressure before an acute cardiac event takes place.

Research question

Public school systems lack objective, scalable mechanisms to identify students at risk of dropping out before upper secondary enrollment, delaying support until academic failure has already occurred.

Participants

N = 186,293 lower secondary graduates across Mexico City tracked longitudinally from 2008–2009 to 2012.

Study Design

Ordinary Least Squares (OLS) regression analysis predicting on-time upper secondary completion using administrative entrance examination and GPA data.

Results

Lower secondary GPA (coefficient = -0.174, p < 0.01) and standardized entrance examination scores (coefficient = -0.003, p < 0.01) accounted for 19.0% of the total variation in upper secondary completion rates.

Takeaway

Deploy automated screening algorithms using baseline administrative GPA and entrance examination scores to identify and rank the top 20% most vulnerable incoming high school students prior to academic year orientation.

Source and citation

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Plain-English glossary

Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.

Baseline

The starting measurements in a study. Researchers use these to compare what participants were like at the start with what happens later.

ExampleBefore a training programme starts, researchers measure workers’ earnings. That first measurement is the baseline.

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