Papers by Wei-Ling Hsu

2 papers
Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning (2023.findings-emnlp)

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Challenge: After the pandemic, e-learning has become part of mainstream education.
Approach: They propose to integrate large language models (LLMs) into educational settings to enhance students' mathematical problem-solving skills by providing adaptive feedback.
Outcome: The proposed model can generate free-text rationalizations and misinterpret meanings and can also misinterprét students' answers.
MathEDU: Feedback Generation on Problem-Solving Processes for Mathematical Learning Support (2026.eacl-long)

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Challenge: Existing studies have examined the reliability of Large Language Models (LLMs) in grading authentic student problem solving processes and delivering effective feedback.
Approach: They propose to use a dataset to evaluate the reliability of large language models in mathematics and a teacher-written feedback system to improve student problem-solving processes.
Outcome: The proposed model improves in correctness classification, error identification, and feedback generation, but generates a gap from teacher-written feedback.

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