Literaturnachweis - Detailanzeige
Autor/inn/en | Eegdeman, Irene; Cornelisz, Ilja; Meeter, Martijn; van Klaveren, Chris |
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Titel | Identifying False Positives When Targeting Students at Risk of Dropping Out |
Quelle | In: Education Economics, 31 (2023) 3, S.313-325 (13 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Zusatzinformation | ORCID (Eegdeman, Irene) ORCID (Cornelisz, Ilja) ORCID (Meeter, Martijn) ORCID (van Klaveren, Chris) |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 0964-5292 |
DOI | 10.1080/09645292.2022.2067131 |
Schlagwörter | Foreign Countries; Vocational Schools; Dropout Characteristics; Dropout Prevention; At Risk Students; Identification; Artificial Intelligence; Algorithms; Prediction; Accuracy; Intervention; Methods; Netherlands |
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. (As Provided). |
Anmerkungen | 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 |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |