MetaCD：基于持续学习的认知诊断元学习框架
MetaCD: A Meta Learning Framework for Cognitive Diagnosis based on Continual Learning
AI总结：
MetaCD通过元学习和持续学习方法，提升认知诊断模型在长尾分布和动态数据下的准确率与泛化能力。
摘要：
认知诊断是智能教育中的重要研究课题，旨在通过学生对不同技能掌握程度的评估。迄今为止，许多研究工作已使用深度学习模型来探索学生、问题和技能之间的复杂交互。然而，现有方法的性能经常受到长尾分布和数据动态变化的限制。为了解决这些挑战，我们提出了一种基于持续学习的认知诊断元学习框架（MetaCD）。该框架通过元学习学习最优的初始化状态来缓解长尾问题，使模型能够在仅有少量数据的情况下实现良好的准确率。此外，我们利用一种名为参数保护机制的持续学习方法，使MetaCD具备适应新技能或新任务的能力，以适应数据的动态变化。MetaCD不仅能提高模型在单任务上的可塑性，还能确保模型在连续任务上的稳定性和泛化能力。在五个真实世界数据集上的综合实验表明，MetaCD在准确率和泛化能力上均优于其他基线方法。
英文摘要：
Cognitive diagnosis is an essential research topic in intelligent education, aimed at assessing the level of mastery of different skills by students. So far, many research works have used deep learning models to explore the complex interactions between students, questions, and skills. However, the performance of existing method is frequently limited by the long-tailed distribution and dynamic changes in the data. To address these challenges, we propose a meta-learning framework for cognitive diagnosis based on continual learning (MetaCD). This framework can alleviate the long-tailed problem by utilizing meta-learning to learn the optimal initialization state, enabling the model to achieve good accuracy on new tasks with only a small amount of data. In addition, we utilize a continual learning method named parameter protection mechanism to give MetaCD the ability to adapt to new skills or new tasks, in order to adapt to dynamic changes in data. MetaCD can not only improve the plasticity of our model on a single task, but also ensure the stability and generalization of the model on sequential tasks. Comprehensive experiments on five real-world datasets show that MetaCD outperforms other baselines in both accuracy and generalization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
