MetaCD: A Meta Learning Framework for Cognitive Diagnosis based on Continual Learning

TL;DR

MetaCD introduces a meta-learning framework that enhances cognitive diagnosis by addressing data imbalance and dynamic skill changes, improving accuracy and generalization in educational assessments.

Contribution

The paper presents a novel meta-learning framework with continual learning mechanisms tailored for cognitive diagnosis, effectively handling long-tailed data and evolving skills.

Findings

MetaCD outperforms baseline models in accuracy across five datasets.

The framework effectively manages data imbalance and skill evolution.

MetaCD maintains stability and generalization in sequential tasks.

Abstract

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.