Summary of Automated Knowledge Concept Annotation and Question Representation Learning For Knowledge Tracing, by Yilmazcan Ozyurt et al.
Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing
by Yilmazcan Ozyurt, Stefan Feuerriegel, Mrinmaya Sachan
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes KCQRL, a framework for automated knowledge concept annotation and question representation learning to improve the effectiveness of existing knowledge tracing (KT) models. The proposed approach addresses two major limitations of current KT methods: relying on expert-defined knowledge concepts in questions and overlooking the semantics of both questions and given concepts. To achieve this, the authors propose an automated KC annotation process using large language models that generates question solutions and annotates KCs, as well as a contrastive learning approach to generate semantically rich embeddings for questions and solution steps aligned with their associated KCs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make personalized learning more effective by creating better ways to understand how students learn. Current methods are limited because they rely on experts defining what students know, which takes time and can be incorrect. They also don’t consider the meaning of questions or concepts. The authors developed a new approach that uses large language models to automatically define concepts in questions and create meaningful representations for them. This can be used with many existing learning systems. The results show that this approach improves performance on two large math learning datasets. |
Keywords
» Artificial intelligence » Representation learning » Semantics
