知识关系等级增强异质学习互动建模用于神经图忘记知识追踪
Linqing Li1, Zhifeng Wang1,2
1Central China Normal University Wollongong Joint Institute, Central China Normal University, Wuhan, China.
PloS one
|December 22, 2023
概括
这项研究引入了一种新的知识追踪模型,通过更好地理解学生的学习来改善教育数据挖掘. 改进的模型减少了偏差,并捕捉了复杂的关系,以便更准确的预测.
科学领域:
- 教育数据挖掘教育数据挖掘
- 教育中的人工智能
- 机器学习用于学习分析.
背景情况:
- 传统的知识追踪模型往往过于简化了练习-知识关系,导致主观偏见和有限的准确性.
- 现有的模型很难捕捉学生,练习和技能之间的微妙互动.
- 自我注意知识追踪模型强调了运动知识关系的重要性,但仍然存在局限性.
研究的目的:
- 提出一种新的知识追踪模型,解决现有方法的局限性.
- 为了减轻知识追踪中的主观标签偏见.
- 通过建模复杂的相互作用来提高预测学生表现的准确性.
主要方法:
- 开发了一个知识关系等级增强异质学习交互建模用于神经图忘记知识跟踪 (KRR-HLIM-NGFT) 模型.
- 使用图形卷积网络 (GCNs) 来建模复杂的学生-练习-技能相互作用.
- 使用知识关系重要性排名校准方法来微调技能关系和Q矩阵,减少偏差.
主要成果:
- 拟议的KRR-HLIM-NGFT模型在两个公共数据集上的基线模型相比显示出更高的性能.
- 该模型在三个关键评估指标上显示了更高的准确性.
- 微调矩阵和GCN有效地捕获了复杂的学习动态.
结论:
- 新的知识追踪模型显著提高了教育环境中的预测准确性.
- 这种方法有效地减少了传统知识追踪方法固有的主观偏见.
- 这项工作为理解和建模学生学习过程提供了更强大的框架.
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