Summary of Explainable Few-shot Knowledge Tracing, by Haoxuan Li and Jifan Yu and Yuanxin Ouyang and Zhuang Liu and Wenge Rong and Juanzi Li and Zhang Xiong
Explainable Few-shot Knowledge Tracing
by Haoxuan Li, Jifan Yu, Yuanxin Ouyang, Zhuang Liu, Wenge Rong, Juanzi Li, Zhang Xiong
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 proposes a new task formulation, Explainable Few-shot Knowledge Tracing, which leverages large language models (LLMs) to track student knowledge from limited practice records while providing natural language explanations. The goal is to fill the gap between current KT tasks and real-world teaching scenarios, where teachers assess students’ knowledge state from limited practices and provide explanatory feedback. Experimental results on three widely used datasets show that LLMs can perform comparable or superior to competitive deep knowledge tracing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve how we use AI in education. Currently, AI is great at predicting student performance, but it doesn’t explain why students got certain answers right or wrong. The goal of this project is to create a system that not only predicts student performance but also explains the reasoning behind those predictions. This will help teachers understand what their students know and don’t know, which can inform how they teach. |
Keywords
» Artificial intelligence » Few shot
