---
license: cc-by-nc-sa-4.0
task_categories:
  - visual-question-answering
  - image-classification
  - text-generation
language:
  - zh
tags:
  - education
  - math
  - error-analysis
  - handwritten
  - multimodal
  - scratchwork
pretty_name: ScratchMath
size_categories:
  - 1K<n<10K
configs:
  - config_name: primary
    data_files: "primary/data-*.parquet"
  - config_name: middle
    data_files: "middle/data-*.parquet"
dataset_info:
  - config_name: primary
    features:
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: solution
        dtype: string
      - name: student_answer
        dtype: string
      - name: student_scratchwork
        dtype: image
      - name: error_category
        dtype:
          class_label:
            names:
              0: 计算错误
              1: 题目理解错误
              2: 知识点错误
              3: 答题技巧错误
              4: 手写誊抄错误
              5: 逻辑推理错误
              6: 注意力与细节错误
      - name: error_explanation
        dtype: string
    splits:
      - name: train
        num_examples: 1479
  - config_name: middle
    features:
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: solution
        dtype: string
      - name: student_answer
        dtype: string
      - name: student_scratchwork
        dtype: image
      - name: error_category
        dtype:
          class_label:
            names:
              0: 计算错误
              1: 题目理解错误
              2: 知识点错误
              3: 答题技巧错误
              4: 手写誊抄错误
              5: 逻辑推理错误
              6: 注意力与细节错误
      - name: error_explanation
        dtype: string
    splits:
      - name: train
        num_examples: 241
---

<div align="center">

# ScratchMath

### *Can MLLMs Read Students' Minds?* Unpacking Multimodal Error Analysis in Handwritten Math

**AIED 2026** &mdash; 27th International Conference on Artificial Intelligence in Education

[![Project Page](https://img.shields.io/badge/Project-Page-blue?style=for-the-badge&logo=googlechrome&logoColor=white)](https://bbsngg.github.io/ScratchMath/)
[![Paper](https://img.shields.io/badge/Paper-PDF-red?style=for-the-badge&logo=adobeacrobatreader&logoColor=white)](https://bbsngg.github.io/ScratchMath/paper/ScratchMath_AIED2026.pdf)
[![Code](https://img.shields.io/badge/Code-GitHub-black?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ai-for-edu/ScratchMath)
[![License](https://img.shields.io/badge/License-CC_BY--NC--SA_4.0-green?style=for-the-badge)](https://creativecommons.org/licenses/by-nc-sa/4.0/)

</div>

---

## Overview

**ScratchMath** is a multimodal benchmark for evaluating whether MLLMs can analyze handwritten mathematical scratchwork produced by real students. Unlike existing math benchmarks that focus on problem-solving accuracy, ScratchMath targets **error diagnosis** &mdash; identifying what type of mistake a student made and explaining why.

- **1,720** authentic student scratchwork samples from Chinese primary & middle schools
- **7** expert-defined error categories with detailed explanations
- **2** complementary tasks: Error Cause Explanation (ECE) & Error Cause Classification (ECC)
- **16** leading MLLMs benchmarked; best model reaches **57.2%** vs. human experts at **83.9%**

---

## Dataset Structure

### Subsets

| Subset | Grade Level | Samples |
|:------:|:-----------:|:-------:|
| `primary` | Grades 1&ndash;6 | 1,479 |
| `middle` | Grades 7&ndash;9 | 241 |

### Error Categories

| Category (zh) | Category (en) | Primary | Middle |
|:-:|:-:|:-:|:-:|
| 计算错误 | Calculation Error | 453 | 113 |
| 题目理解错误 | Problem Comprehension Error | 499 | 20 |
| 知识点错误 | Conceptual Knowledge Error | 174 | 45 |
| 答题技巧错误 | Procedural Error | 118 | 17 |
| 手写誊抄错误 | Transcription Error | 95 | 29 |
| 逻辑推理错误 | Logical Reasoning Error | 73 | 2 |
| 注意力与细节错误 | Attention & Detail Error | 67 | 15 |

### Fields

| Field | Type | Description |
|:------|:----:|:------------|
| `question_id` | string | Unique identifier |
| `question` | string | Math problem text (may contain LaTeX) |
| `answer` | string | Correct answer |
| `solution` | string | Step-by-step reference solution |
| `student_answer` | string | Student's incorrect answer |
| `student_scratchwork` | image | Photo of handwritten work |
| `error_category` | ClassLabel | One of 7 error types |
| `error_explanation` | string | Expert explanation of the error |

---

## Quick Start

```python
from datasets import load_dataset

# Load primary school subset
ds_primary = load_dataset("songdj/ScratchMath", "primary")

# Load middle school subset
ds_middle = load_dataset("songdj/ScratchMath", "middle")

# Access a sample
sample = ds_primary["train"][0]
print(sample["question"])
print(sample["error_category"])
sample["student_scratchwork"].show()
```

---

## Citation

If you use this dataset, please cite:

```bibtex
@inproceedings{song2026scratchmath,
  title     = {Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math},
  author    = {Song, Dingjie and Xu, Tianlong and Zhang, Yi-Fan and Li, Hang and Yan, Zhiling and Fan, Xing and Li, Haoyang and Sun, Lichao and Wen, Qingsong},
  booktitle = {Proceedings of the 27th International Conference on Artificial Intelligence in Education (AIED)},
  year      = {2026}
}
```

---

## License

This dataset is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.