什么因素可以提高学生的成绩? 一种机器学习和可解释方法的方法
Hui Mao1,2, Ribesh Khanal1, ChengZhang Qu2
1School of Economics and Management, China Three Gorges University, Yichang, People's Republic of China.
PloS one
|May 16, 2025
概括
机器学习模型显示,学生的行为和教学方法显著影响学业成绩. 优化学习需要平衡直接指导与主动学习和技术整合.
科学领域:
- 教育技术的教育技术.
- 学习分析学习分析
- 教育中的人工智能
背景情况:
- 传统的研究往往忽视了影响学生成绩的复杂相互作用.
- 现有的研究经常分析孤立的因素或简单的相关性,缺少多变量关系.
研究的目的:
- 模拟行为和教学预测因素与学生成绩之间的多变量关系.
- 利用可解释的人工智能来揭示细微的因素-成就动态.
- 为改善教学策略和学生支持提供可操作的见解.
主要方法:
- 采用了五个机器学习算法 (SVM,DT,ANN,RF,XGBoost) 的组合.
- 模拟了四个行为和六个指令预测器之间的关系,使用最终考试成绩作为结果.
- 应用可解释的人工智能技术来识别关键模式和因素贡献.
主要成果:
- 具有可解释性的机器学习有效地识别了细微的因素-成就关系.
- 行为指标 (家庭作业,回答,讨论,出席分数) 始终显示出与成就的积极关联.
- 高成绩的学生表现出更强的协作技能和对技术增强的学习环境的偏好.
- 游戏化频率对结果产生了积极的影响,而分配频率则产生了反作用.
结论:
- 教师应该平衡直接指导与积极学习模式,以优化学生的成绩.
- 预测分析,利用可识别的学习特征,可以通知预警系统主动支持学生.
- 开发的框架有助于将预测分析转化为实际的教学改进.
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