蒙特卡洛优化在不平衡数据中进行抽样选择,应用于学生学预测
Dianela Herrera1, Nicolás Ángel1, Diego González1
1Departamento de Física, Universidad Católica del Norte, Av. Angamos 0610, Antofagasta 1240000, Chile.
Chaos (Woodbury, N.Y.)
|October 17, 2025
概括
这项研究引入了一种机器学习工具,用于预测大学生学风险. 通过分析学生数据和使用蒙特卡洛方法来发现失衡,它旨在为处于危险的学生提供早期干预.
科学领域:
- 教育数据挖掘教育数据挖掘
- 机器学习在教育中的应用
- 高等教育的分析.
背景情况:
- 学生学是一个影响学术和专业课程的全球性问题.
- 现有的干预措施往往将学术成绩优先于其他重要因素.
- 早期识别有风险的学生对于有效的支持至关重要.
研究的目的:
- 开发和评估一款机器学习工具,用于早期识别面临高退学风险的大学生.
- 为了应对数据挑战,特别是第一年学生数据中的阶级不平衡.
- 为那些面临退学风险的学生提供及时有效的预防性干预措施.
主要方法:
- 使用来自北方天主教大学的大型数据集.
- 应用并测试了各种机器学习算法以获得预测能力.
- 实施蒙特卡洛方法来调整第一年数据中的阶级不平衡.
主要成果:
- 机器学习工具在识别学风险的学生方面显示出预测效用.
- 蒙特卡洛调整在不平衡条件下显著改善了模型性能,特别是对于第一年学的人.
- 在所有研究的案例中都观察到模型性能改善的一般水平.
结论:
- 开发的机器学习工具对早期发现学生学风险充满希望.
- 像蒙特卡洛这样的创新数据调整技术在教育分析中有效处理不平衡的数据集.
- 早期识别使得有针对性的干预措施成为可能,从而有可能提高学生留学率和学业成功.
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