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.
Original language | English |
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Pages (from-to) | 313-325 |
Number of pages | 13 |
Journal | Education Economics |
Volume | 31 |
Issue number | 3 |
Early online date | 23 Apr 2022 |
DOIs | |
Publication status | Published - 4 May 2023 |
Keywords
- Study succes
- Machine learning
- Vocational education