教育数据的可见度图分析:预测面临风险的在线学生的潜力和案例研究
Hadis Azizi1, Mohammad Sadra Amini1, Sadegh Sulaimany2
1Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran.
Scientific reports
|August 31, 2025
概括
视觉图分析将在线学习数据转化为图形, 这种教育数据挖掘方法提供了可解释的洞察力,并且优于深度学习方法.
科学领域:
- 教育数据挖掘
- 网络科学
- 机器学习
背景情况:
- 在线学习产生了大量的时间数据.
- 分析学生的互动对于识别学习模式和预测结果至关重要.
- 传统的方法可能无法完全捕捉学生在数字环境中的复杂行为.
研究的目的:
- 引入和评估教育时间序列数据的可见度图分析.
- 在线学习环境中预测有风险的学生.
- 通过图形理论特征提供对学生行为可解释的见解.
主要方法:
- 将教育时间序列数据转换为可见度图.
- 图形理论指标的应用 (例如,全球效率,分类性,中间中心性).
- 使用渐变增强算法对有风险的学生进行分类.
主要成果:
- 基于点击流数据, 可见度图分析准确地预测有风险的在线学生.
- 使用梯度提升实现了超过87%的分类准确性.
- 该方法提供了可解释的学生行为见解, 优于一些深度学习方法.
结论:
- 可见度图分析是教育数据挖掘的宝贵补充工具.
- 它提供可解释的见解和有效的学习结果预测.
- 需要进一步研究以优化特定教育数据集的模型和图表选择.
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