通过使用多式联网数据,评估用于高等教育中公平和可解释的预测的集合模型
Felipe Emiliano Arévalo-Cordovilla1,2, Marta Peña3
1Faculty of Science and Engineering, Universidad Estatal de Milagro, Ciudadela Universitaria "Dr. Rómulo Minchala Murillo", km. 1.5 vía Milagro - Virgen de Fátima, Milagro, 091050, Ecuador. farevaloc@unemi.edu.ec.
Scientific reports
|August 11, 2025
概括
这项研究使用Moodle数据开发了一种在线学生成功的预测模型. 早期成绩是关键预测因素,使早期干预能够减少高等教育的消耗.
科学领域:
- 学习分析学习分析
- 教育数据挖掘教育数据挖掘
- 高等教育研究 高等教育研究
背景情况:
- 在线高等教育面临的挑战是学生缩.
- 现有的预测模型往往缺乏全面的数据整合和与先进技术的比较.
- 需要强大,可解释和公平的模型来预测学业绩的早期预测.
研究的目的:
- 开发和评估用于在线工程课程中早期识别有风险的学生的预测框架.
- 整合各种数据源,包括Moodle交互,学术历史和人口统计.
- 为了比较各种机器学习模型的性能,并确定学术成功的关键预测因素.
主要方法:
- 利用了2225名工程学生的数据,包括Moodle日志,学术记录和人口统计数据.
- 应用SMOTE (合成少数人过量采样技术) 进行类平衡.
- 评估了7个基础学习者 (包括随机森林,XGBoost,LightGBM) 和使用5倍分层交叉验证的堆叠组合.
- 为了模型的可解释性,使用了SHAP (夏普利添加式解释) 分析.
主要成果:
- 作为基本模型,LightGBM表现出优异的性能 (AUC=0.953,F1=0.950).
- 堆叠组合没有显著改善性能,并且显示不稳定.
- 早期的学业成绩被确定为顶级模型中最有影响力的预测因素.
- 最终的模型在人口群体之间表现出强烈的公平性 (一致性=0.907).
结论:
- 开发的模型有效地预测在线工程学生的学术表现.
- 早期成绩是学生成功的关键指标,通知及时干预.
- 该研究提供了学习分析的最新,可解释和公平的模型,帮助机构减少磨损.
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