Identifying False Positives When Targeting Students at Risk of Dropping Out

Autor/inn/en	Eegdeman, Irene; Cornelisz, Ilja; Meeter, Martijn; van Klaveren, Chris
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 Verfügbarkeit
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 + Suchen Sie Ihr Suchwort? Ausland; Vocational school; Berufsbildende Schule; Berufsschule; Fachschule; Identifikation; Identifizierung; Künstliche Intelligenz; Algorithm; Algorithmus; Vorhersage; Method; Methode; Niederlande
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).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01