一种知识追踪方法,使用双图卷积网络和正/负特征增强网络
Jianjun Wang1, Qianjun Tang2, Zongliang Zheng3
1School of Fine Arts and Design, Leshan Normal University, Leshan, Sichuan, China.
PloS one
|April 9, 2025
概括
这项研究引入了一种使用双图卷积网络的新型知识追踪方法,以改善对学生掌握的预测. 该方法有效地处理大量的技能和复杂的学习数据,以获得更好的教育见解.
科学领域:
- 教育数据挖掘教育数据挖掘
- 教育中的人工智能
- 机器学习 机器学习
背景情况:
- 知识追踪模型对于预测学生掌握技能至关重要.
- 现有的模型面临着可扩展性,数据稀疏性,个体学习差异和技能相互依赖性的挑战.
研究的目的:
- 开发一种先进的知识追踪方法,解决当前方法的局限性.
- 在复杂的教育环境中提高学生掌握预测的准确性和稳定性.
主要方法:
- 提出了一种使用双图卷积网络 (GCN) 和正负特征增强的新型知识追踪方法.
- 构建了代表学生和技能的双图形结构,由GCN独立处理.
- 集成功能增强和光谱嵌入集群,以实现高效的功能组合和优化.
主要成果:
- 拟议的双GCN方法显著优于公开数据集上的现有知识追踪方法.
- 在处理数据稀疏性和复杂技能相关性方面表现出卓越的表现.
- 有效地解决了个体学习表现的差异.
结论:
- 开发的方法为教育数据挖掘和个性化学习提供了有希望的进步.
- 突出了图形学习模型的潜力,并在教育应用中突出了功能增强技术.
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