Meta-Learning for Cognitive Diagnosis with Continual Learning

This paper addresses the challenges of long-tailed data distributions and dynamic changes in cognitive diagnosis, a crucial area in intelligent education. It proposes a novel meta-learning framework (MetaCD) that leverages continual learning to improve model performance on new tasks with limited data and adapt to evolving skill sets. The use of meta-learning for initialization and a parameter protection mechanism for continual learning are key contributions. The paper's significance lies in its potential to enhance the accuracy and adaptability of cognitive diagnosis models in real-world educational settings.

• •Proposes MetaCD, a meta-learning framework for cognitive diagnosis.
• •Addresses long-tailed data and dynamic changes in educational data.
• •Utilizes meta-learning for initialization and continual learning for adaptation.
• •Demonstrates improved accuracy and generalization on real-world datasets.