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采矿自主学生模式在线高等教育中在LMS上得分

Ricardo Ordoñez-Avila^1,2, Jaime Meza¹, Sebastian Ventura²

¹Departamento de Sistemas Computacionales, Universidad Técnica de Manabí, Portoviejo, Manabí, Ecuador.

PeerJ. Computer science

|June 26, 2025

概括

数据挖掘利用学习管理系统 (LMS) 数据预测在线法律和心理学课程中的学生自主成绩. 渐变增强和XGBoost模型显示出强大的预测性能,为个性化的在线学习改进提供了洞察力.

科学领域:

教育技术的教育技术.
数据科学数据科学数据科学
高等教育高等教育

背景情况:

学习管理系统 (LMS) 是高等教育在线教育的组成部分.
预测学生自主成绩对于理解和增强在线学习体验至关重要.

研究的目的:

应用数据挖掘技术来预测在线法律和心理学课程中的学生自主成绩.
确定影响在LMS环境中的自主学习的关键学生参与变量.

主要方法:

利用了来自在线法律和心理学课程的16000多个学生记录的数据集.
采用数据预处理,RobustScaler转换和递归特征消除与交叉验证 (RFEcv) 来进行特征选择.
实施和评估预测模型,包括梯度提升和极端梯度提升 (XGBoost),优化超参数.

主要成果:

递归特征消除与交叉验证 (RFEcv) 显著改善了模型性能.
梯度提升模型在法律课程中获得了最高的R平方 (0.6693) 和心理学 (R平方 = 0.6418) 的强结果.
在结合两个数据集时,XGBoost模型表现最好 (R-平方 = 0.6294).

结论:

数据挖掘,特别是通过RFEcv和超参数调进行特征选择,有效地预测了在线学习中的学生自主得分.
这些发现表明,LMS数据可以为个性化的在线学习改进提供实时指标.
建议在未来将自主得分数据直接集成到LMS平台中,以获得更深入的见解.

关键词:

自主评分独立评分教育数据挖掘教育数据挖掘在LMS中使用LMS.学习分析学习分析.模式模式模式虚拟教育是虚拟的教育.

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