Machine Learning Predicts Upper Secondary Education Dropout as Early as the End of Primary School
CoRR(2024)
摘要
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.
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