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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- math |
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pretty_name: bridge |
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size_categories: |
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- n<1K |
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--- |
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TLDR: This dataset is a real-world math tutoring dataset from the NAACL 2024 paper ``Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes''. |
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The dataset targets scenarios where the student makes a math mistake. |
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- `c_h` is the conversation history |
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- `c_r` is the original tutor's response |
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- `c_r_` is the experienced teacher's response |
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Optionally, there is other interesting metadata from our Bridge method: |
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- `e` is the student error type that the experienced teacher identified |
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- `z_what` is the strategy that the experienced teacher wants to use in their response |
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- `z_why` is the intention that the experienced teacher wants to achieve in their response |
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# 🌁 Bridging the Novice-Expert Gap via Models of Decision-Making |
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[Paper Link](https://arxiv.org/abs/2310.10648), [Code Link](https://github.com/rosewang2008/bridge/) |
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**NAACL 2024** |
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**Title:** Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes |
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**Authors:** Rose E. Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb, Dorottya Demszky |
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**Main Idea: We contribute Bridge 🌁, a method that uses cognitive task analysis to translate an expert's implicit thought process into an explicit decision-making model**. |
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Scaling high-quality tutoring remains a major challenge in education. |
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Due to growing demand, many platforms employ novice tutors who, unlike experienced educators, struggle to address student mistakes and thus fail to seize prime learning opportunities. |
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Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes. |
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**Bridge 🌁 leverages cognitive task analysis to model an expert's internal decision-making in remediation: Experts internally identify (A) the student's error, (B) a remediation strategy, and (C) their intention before generating a response.** |
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We construct a dataset of 700 real tutoring conversations, annotated by experts with their decisions. |
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We evaluate state-of-the-art LLMs on our dataset and find that the expert's decision-making model is critical for LLMs to close the gap: |
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responses from GPT4 with expert decisions (e.g., ``simplify the problem'') are +76% more preferred than without. |
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Additionally, context-sensitive decisions are critical to closing pedagogical gaps: |
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random decisions decrease GPT4's response quality by -97% than expert decisions. |
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Our work shows the potential of embedding expert thought processes in LLM generations to enhance their capability to bridge novice-expert knowledge gaps. |
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For more information about how the dataset is curated, please check out our codebase: https://github.com/rosewang2008/bridge/, and paper: https://arxiv.org/pdf/2310.10648 |
