Identifying false positives when targeting students at risk of dropping out

Irene Eegdeman, Ilja Cornelisz, Martijn Meeter and Chris Klaveren

Education Economics, 2023, vol. 31, issue 3, 313-325

Abstract: Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for dropout prevention explicit. Data of a Dutch vocational education institute is used to show how out-of-sample machine learning predictions can be used to formulate invitation rules in a way that targets students at risk more effectively, thereby facilitating early detection for effective dropout prevention.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:edecon:v:31:y:2023:i:3:p:313-325

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DOI: 10.1080/09645292.2022.2067131

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