Identifying False Positives When Targeting Students at Risk of Dropping Out
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| Title: | Identifying False Positives When Targeting Students at Risk of Dropping Out |
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| Language: | English |
| Authors: | Eegdeman, Irene (ORCID |
| Source: | Education Economics. 2023 31(3):313-325. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 13 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Foreign Countries, Vocational Schools, Dropout Characteristics, Dropout Prevention, At Risk Students, Identification, Artificial Intelligence, Algorithms, Prediction, Accuracy, Intervention, Methods |
| Geographic Terms: | Netherlands |
| DOI: | 10.1080/09645292.2022.2067131 |
| ISSN: | 0964-5292 1469-5782 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1387783 |
| Database: | ERIC |
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| 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. |
|---|---|
| ISSN: | 0964-5292 1469-5782 |
| DOI: | 10.1080/09645292.2022.2067131 |
