用机器学习提前识别可能退学的学生，帮高校及时干预。
Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms
- 基于学业成绩、人口统计和入学记录训练模型
- 逻辑回归与线性SVM准确率最高，效果显著
- 适合教育机构做学生留校预警，提升留存率
学生流失对高等教育机构构成重大挑战，影响学术成果与财务可持续性。机器学习可有效识别需帮助的学生，实现早期干预。本研究评估了逻辑回归、随机森林、支持向量机（SVM）和K近邻（KNN）等算法，基于学业表现、人口统计信息及注册记录预测学生退学与取消注册行为。结果表明，逻辑回归与线性SVM模型表现最优，证明了机器学习在识别高风险学生方面的有效性。
原文摘要 · Abstract (English)
Student attrition represents a significant challenge for higher education institutions because it impacts both academic results and financial viability. Machine learning provides an effective solution to identify students who require assistance before they leave their academic programs. The research investigates how machine learning approaches enable institutions to predict student withdrawal and enrollment cancellation through data-driven insights for strategic decisionmaking. The evaluation of models includes Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) based on academic performance and demographic data and enrollment records. The results show that logistic regression and linear SVM models produced the highest accuracy which demonstrates ML's capability to detect students at risk.
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