README.md

---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
tags:
- math
pretty_name: bridge
size_categories:
- n<1K
---
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''.
The dataset targets scenarios where the student makes a math mistake.

- `c_h` is the conversation history
- `c_r` is the original tutor's response
- `c_r_` is the experienced teacher's response

Optionally, there is other interesting metadata from our Bridge method: 
- `e` is the student error type that the experienced teacher identified
- `z_what` is the strategy that the experienced teacher wants to use in their response
- `z_why` is the intention that the experienced teacher wants to achieve in their response

With the metadata, you can replicate our model of teacher's internal decision thoughts: 

![Main Figure](fig1.png)

# 🌁 Bridging the Novice-Expert Gap via Models of Decision-Making

[Paper Link](https://arxiv.org/abs/2310.10648), [Code Link](https://github.com/rosewang2008/bridge/)

**NAACL 2024**

**Title:** Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes

**Authors:** Rose E. Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb, Dorottya Demszky

**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**.

Scaling high-quality tutoring remains a major challenge in education.
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.
Our work explores the potential of large language models (LLMs) to close the novice-expert knowledge gap in remediating math mistakes.
**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.**
We construct a dataset of 700 real tutoring conversations, annotated by experts with their decisions.
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: 
responses from GPT4 with expert decisions (e.g., ``simplify the problem'') are +76% more preferred than without.
Additionally, context-sensitive decisions are critical to closing pedagogical gaps: 
random decisions decrease GPT4's response quality by -97% than expert decisions.
Our work shows the potential of embedding expert thought processes in LLM generations to enhance their capability to bridge novice-expert knowledge gaps. 


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