Summary of Domain Generalizable Knowledge Tracing Via Concept Aggregation and Relation-based Attention, by Yuquan Xie et al.
Domain Generalizable Knowledge Tracing via Concept Aggregation and Relation-Based Attention
by Yuquan Xie, Wanqi Yang, Jinyu Wei, Ming Yang, Yang Gao
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to address the limitations of current knowledge tracing methods in online education systems. By leveraging student interactions from existing education systems, the authors propose a domain generalization method for knowledge tracing, which can be applied to any KT model. The framework, called DGKT, incorporates a concept aggregation approach and a normalization module called Sequence Instance Normalization (SeqIN) to reduce conceptual disparities and domain discrepancies. Additionally, the authors introduce a new knowledge tracing model tailored for the domain generalization KT task, named Domain-Generalizable Relation-based Knowledge Tracing (DGRKT). Experimental results on five benchmark datasets demonstrate that the proposed method performs well despite limited training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about improving how computers learn about students’ knowledge and skills. Right now, computers can only do this by looking at what students have done before. But sometimes there’s not enough information for them to make good predictions. To fix this, the authors use information from other places where students have interacted with similar things, like exercises or questions. They also design a new way of combining and normalizing this data to help computers better understand students’ knowledge. This can be used in many different online learning systems. |
Keywords
» Artificial intelligence » Domain generalization » Online learning
