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.

教育在减轻贫困、推动经济增长和赋予个人力量方面发挥着关键作用，然而，学校辍学的持续问题带来了重大挑战，本研究在 13 年的纵向数据集基础上通过机器学习模型的开发展现出显著的分类能力，未来这样的模型有可能支持教育工作者识别处于风险之中的学生，以改善教育结果。