机器学习方法对学生的表现进行预测 在线学习的在线学习
PloS one
|January 14, 2025
概括
本研究介绍了一种机器学习模型,用于预测在线学习中的学生表现. 该模型识别了关键的学习行为,与其他方法相比,提高了预测准确度.
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
背景情况:
- 学生的成绩对于教育系统的改善至关重要.
- 教育数据挖掘利用数据获得更好的学习成果.
研究的目的:
- 提出一种机器学习方法,用于预测在线学习中的学生表现.
- 识别和利用关键的学习行为指标,以准确预测.
主要方法:
- 从在线学习过程中构建了11个学习行为指标.
- 根据与学生成绩的相关性来过指标,将强烈相关的指标保留为自身值.
- 训练了一个逻辑回归模型与泰勒扩展使用选定的自值指标.
主要成果:
- 拟议的物流回归模型表现出比较模型更优越的预测能力.
- 发现学生学习主动性和学习持续时间之间存在显著的依赖.
- 学习时间显著影响学生的成绩预测.
结论:
- 开发的机器学习方法有效地预测了在线学习环境中的学生表现.
- 特定的学习行为,特别是学习持续时间和主动性,是关键的预测因素.
- 这项研究通过数据驱动的洞察力,有助于增强教育系统.
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