Machine learning predicts upper secondary education dropout as early as the end of primary school.
Academic outcomes
Comprehensive education
Education dropout
Kindergarten
Longitudinal data
Machine learning
Upper secondary education
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 06 2024
05 06 2024
Historique:
received:
27
02
2024
accepted:
30
05
2024
medline:
6
6
2024
pubmed:
6
6
2024
entrez:
5
6
2024
Statut:
epublish
Résumé
Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students' academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators' processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.
Identifiants
pubmed: 38839872
doi: 10.1038/s41598-024-63629-0
pii: 10.1038/s41598-024-63629-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12956Subventions
Organisme : Research Council of Finland
ID : 339418
Organisme : Research Council of Finland
ID : 276239
Organisme : Research Council of Finland
ID : 323773
Organisme : Strategic Research Council
ID : 352648
Informations de copyright
© 2024. The Author(s).
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