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统计和机器学习模型用于预测大学学和奖学金影响
1Department of Mathematics and Statistics, San Diego State University, San Diego, California, United States of America.
PloS one
|June 25, 2025
概括
学生学风险可以通过学术和社会经济因素预测. 奖学金显著降低了学率,XGBoost模型在识别有风险的学生方面显示出高准确度.
科学领域:
- 高等教育研究 高等教育研究
- 教育数据挖掘教育数据挖掘
- 留学生研究 留学生研究
背景情况:
- 学生学是高等教育的一个持续的挑战.
- 了解退学预测因素对于制定有效的干预策略至关重要.
- 以前的研究往往缺乏对财政援助对保留影响的因果分析.
研究的目的:
- 确定预测学生学风险的关键社会经济和学术特征.
- 以因果评估奖学金奖项对留学生的影响.
- 为了比较各种机器学习模型在预测学中的表现.
主要方法:
- 分析了来自葡萄牙高等教育机构的4424名学生记录.
- 退学的定义包括领域和机构的变化.
- 倾向性得分匹配用于估计奖学金的因果关系.
- 评估的分类器:拉索回归,通用添加模型 (GAM),随机森林,XGBoost和神经网络.
主要成果:
- XGBoost模型获得了最高的F1得分 (0.904).
- 关键预测因素包括第二学期成绩,学分单位,学费支付状态,债务,奖学金持有率和入学年龄.
- GAM分析表明,奖学金可以将学率降低约40% (22.2%的概率降低).
结论:
- 学业成绩和财务状况是导致学生学的重要因素.
- 奖学金计划显然提高了学生的留学能力.
- 机器学习模型,特别是XGBoost,为预测学风险提供了强大的工具.
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