Machine learning predicts upper secondary education dropout as early as the end of primary school.

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Machine Learning* ; Student Dropouts*/statistics & numerical data ; Schools*; Humans ; Child ; Adolescent ; Female ; Male ; Longitudinal Studies ; Students/psychology
    • Abstract:
      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.
      (© 2024. The Author(s).)
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    • Grant Information:
      339418 Research Council of Finland; 276239 Research Council of Finland; 323773 Research Council of Finland; 352648 Strategic Research Council
    • Contributed Indexing:
      Keywords: Academic outcomes; Comprehensive education; Education dropout; Kindergarten; Longitudinal data; Machine learning; Upper secondary education
    • Publication Date:
      Date Created: 20240605 Date Completed: 20240605 Latest Revision: 20240608
    • Publication Date:
      20240608
    • Accession Number:
      PMC11153526
    • Accession Number:
      10.1038/s41598-024-63629-0
    • Accession Number:
      38839872