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。

机器学习早在小学毕业就能预测中学辍学

Machine Learning Predicts Upper Secondary Education Dropout as Early as  the End of Primary School

An enduring issue in higher education is student retention to successful
graduation. National statistics indicate that most higher education
institutions have four-year degree completion rates around 50 percent, or just
half of their student populations. While there are prediction models which
illuminate what factors assist with college student success, interventions that
support course selections on a semester-to-semester basis have yet to be deeply
understood. To further this goal, we develop a system to predict students'
grades in the courses they will enroll in during the next enrollment term by
learning patterns from historical transcript data coupled with additional
information about students, courses and the instructors teaching them.
We explore a variety of classic and state-of-the-art techniques which have
proven effective for recommendation tasks in the e-commerce domain. In our
experiments, Factorization Machines (FM), Random Forests (RF), and the
Personalized Multi-Linear Regression model achieve the lowest prediction error.
Application of a novel feature selection technique is key to the predictive
success and interpretability of the FM. By comparing feature importance across
populations and across models, we uncover strong connections between instructor
characteristics and student performance. We also discover key differences
between transfer and non-transfer students. Ultimately we find that a hybrid
FM-RF method can be used to accurately predict grades for both new and
returning students taking both new and existing courses. Application of these
techniques holds promise for student degree planning, instructor interventions,
and personalized advising, all of which could improve retention and academic
performance.

研究了高等教育中学生保留率的问题，通过学习历史记录数据和其他信息预测学生成绩，发现了教师特征和学生表现之间的强烈联系，探索了预测模型并发现了能够准确预测成绩的混合方法。