Beyond Early Warning Indicators: High School Dropout and Machine Learning

Dario Sansone

Working Papers from Georgetown University, Department of Economics

Abstract: This paper provides an algorithm to predict which students are going to drop out of high schools relying only on information from 9th grade. It verifies that using a parsimonious early warning system - as implemented in many schools - leads to poor results. It shows that schools can obtain more precise predictions by exploiting the available high-dimensional data jointly with machine learning tools such as Support Vector Machine, Boosted Regression and Post-LASSO. It carefully selects goodness-of-fit criteria based on the context and the underlying theoretical framework: model parameters are calibrated by taking into account policy goals and budget constraints. Finally, it uses unsupervised machine learning to divide students at risk of dropping out into different clusters.

Keywords: High School Dropout; Machine Learning; Big Data (search for similar items in EconPapers)
JEL-codes: C53 C55 I20 (search for similar items in EconPapers)
Pages: 49
Date: 2017-12-27
New Economics Papers: this item is included in nep-big, nep-cmp, nep-edu and nep-ure
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Beyond Early Warning Indicators: High School Dropout and Machine Learning (2019)
Working Paper: Now You See Me: High School Dropout and Machine Learning (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:geo:guwopa:gueconwpa~17-17-09

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