DKVMN&MRI:基于DKVMN的新深度知识跟踪模型,包含多关系信息
Feng Xu1, Kang Chen2, Maosheng Zhong2
1Jiangxi Provincial Education Institute, Jiangxi, China.
PloS one
|October 30, 2024
概括
本研究介绍了DKVMN&MRI,这是一种深度知识跟踪模型,通过结合练习-知识,练习-练习和学习-忘记关系来增强预测. 该模型显示智能教育系统的准确性和可解释性得到了显著的改进.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 知识追踪对于智能教育系统至关重要,它可以从历史数据中建模学生的知识状态.
- 现有的模型在稀疏的数据,可解释性和捕捉复杂的练习关系方面扎.
研究的目的:
- 开发一个先进的深度知识跟踪模型 (DKVMN&MRI),解决当前方法的局限性.
- 通过整合多种关系数据类型来提高学生学习能力的预测.
主要方法:
- 利用具有长短期内存 (LSTM) 的动态键值内存网络 (DKVMN) 来建模学习过程.
- 整合了埃宾豪斯忘记曲线来模拟记忆衰退.
- 综合物品响应理论 (IRT) 和预测准确性的注意力机制.
主要成果:
- DKVMN&MRI在三个现实数据集中显示了AUC和ACC指标的显著改善.
- 该模型有效地捕捉了练习-知识点,练习-练习和学习-忘记关系.
- 与最先进的知识跟踪模型相比,实现了更高的性能.
结论:
- DKVMN&MRI提供了一种更准确,更易于解释的知识追踪方法.
- 该模型能够整合多样化的关系,提高其在智能教育中的有效性.
- 提供了对学习者知识状态和炼互动的宝贵见解.
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