Summary of Prompt Transfer For Dual-aspect Cross Domain Cognitive Diagnosis, by Fei Liu et al.
Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis
by Fei Liu, Yizhong Zhang, Shuochen Liu, Shengwei Ji, Kui Yu, Le Wu
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed PromptCD framework addresses the challenges in cross-domain cognitive diagnosis (CDCD) by leveraging soft prompt transfer for cognitive diagnosis. This scenario-agnostic approach is designed to adapt seamlessly across diverse CDCD scenarios, introducing PromptCD-S for student-aspect CDCD and PromptCD-E for exercise-aspect CDCD. The framework achieves superior performance across various CDCD scenarios in extensive experiments on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to evaluate students’ cognitive states using their interaction data. This is important because it can help recommend exercises and provide personalized learning guidance. However, this task is challenging because different scenarios require different approaches. The proposed framework, called PromptCD, solves this problem by adapting to different scenarios without needing to be rewritten for each one. The results show that PromptCD works well across many scenarios and provides a unified approach to cognitive diagnosis. |
Keywords
* Artificial intelligence * Prompt
