在智能辅导系统中基于深度学习的知识跟踪.
Xin Zhou1, Zhuoxu Zhang2, Xike Xie3
1State University of New York at Binghamton, Binghamton, New York, USA.
Scientific reports
|July 2, 2025
概括
本研究引入了一种质量意识的深度学习框架,以解决智能辅导系统 (ITS) 的知识跟踪 (KT) 中的数据稀疏性. 新方法准确地捕捉学生的知识状态,改善个性化的教育交付.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在线教育和智能辅导系统 (ITS) 已经增长,特别是在COVID-19大流行期间.
- 知识跟踪 (KT) 对ITS至关重要,它从交互数据中建模学生的知识状态,以提供个性化的反.
- 深度学习,就像深度知识跟踪一样,已经推进了KT,但往往在数据稀疏性方面扎.
研究的目的:
- 为知识追踪提出一个新的深度学习框架,有效地应对数据稀疏性的挑战.
- 提高教育环境中学生知识状态的建模和预测的准确性.
主要方法:
- 为知识追踪开发了一种质量意识的深度学习框架.
- 该框架结合了稀疏注意力技术和生成解码来管理有限的学生交互数据.
- 拟议的模型是通过对现实世界数据集进行广泛的实验来评估的.
主要成果:
- 提出的质量意识框架证明了准确地捕捉学生的知识状态.
- 该方法有效地解决了现有知识追踪系统中普遍存在的数据稀疏性问题.
- 实验结果验证了框架在各种真实数据集上的性能.
结论:
- 开发的质量意识深度学习框架提供了一个强大的解决方案,用于用稀疏的数据追踪知识.
- 这种方法提高了智能辅导系统准确评估和建模学生学习的能力.
- 这些发现有助于更有效和个性化的在线学习体验.
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