Redesign dataset card with badges, structured layout, and cross-links

README.md

@@ -84,56 +84,71 @@ dataset_info:
         num_examples: 241
 ---
 
+<div align="center">
+
 # ScratchMath
 
-**AIED 2026** | [Project Page](https://bbsngg.github.io/ScratchMath/) | [Paper (PDF)](https://bbsngg.github.io/ScratchMath/paper/ScratchMath_AIED2026.pdf) | [Code (GitHub)](https://github.com/ai-for-edu/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_4.0-green?style=for-the-badge)](https://creativecommons.org/licenses/by/4.0/)
+
+</div>
 
-**ScratchMath** is a multimodal benchmark dataset for evaluating the ability of Multimodal Large Language Models (MLLMs) to analyze handwritten mathematical scratchwork produced by real students. Unlike existing math benchmarks that focus on problem-solving accuracy, ScratchMath targets **error diagnosis** — identifying what type of mistake a student made and explaining why.
+---
+
+## Overview
 
-## Dataset Description
+**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.
 
-The dataset contains 1,720 samples of authentic student scratchwork collected from an online education platform. Each sample includes:
+- **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%**
+
+---
 
-- A math problem with its correct answer and solution
-- The student's incorrect answer
-- A photograph of the student's handwritten scratchwork
-- A human-annotated error category and detailed error explanation
+## Dataset Structure
 
 ### Subsets
 
-| Subset | Description | Samples |
-|--------|-------------|---------|
-| `primary` | Primary school (Grades 1–6) math problems | 1,479 |
-| `middle` | Middle school (Grades 7–9) math problems | 241 |
+| Subset | Grade Level | Samples |
+|:------:|:-----------:|:-------:|
+| `primary` | Grades 1&ndash;6 | 1,479 |
+| `middle` | Grades 7&ndash;9 | 241 |
 
 ### Error Categories
 
-Each sample is labeled with one of seven error categories:
-
-| Category | English | Primary | Middle |
-|----------|---------|---------|--------|
+| Category (zh) | Category (en) | Primary | Middle |
+|:-:|:-:|:-:|:-:|
 | 计算错误 | Calculation Error | 453 | 113 |
-| 题目理解错误 | Question Comprehension Error | 499 | 20 |
-| 知识点错误 | Knowledge Gap Error | 174 | 45 |
-| 答题技巧错误 | Problem-Solving Strategy Error | 118 | 17 |
-| 手写誊抄错误 | Handwriting Transcription Error | 95 | 29 |
+| 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
+### Fields
 
 | Field | Type | Description |
-|-------|------|-------------|
-| `question_id` | string | Unique identifier for the math problem |
-| `question` | string | The math problem text (may contain LaTeX in `$...$` delimiters) |
-| `answer` | string | The correct answer(s) |
-| `solution` | string | Step-by-step solution with explanation |
-| `student_answer` | string | The student's incorrect answer |
-| `student_scratchwork` | image | Photograph of the student's handwritten work |
-| `error_category` | ClassLabel | One of 7 error types (see above) |
-| `error_explanation` | string | Detailed explanation of what error the student made and why |
-
-## Usage
+|:------|:----:|:------------|
+| `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
@@ -148,22 +163,26 @@ ds_middle = load_dataset("songdj/ScratchMath", "middle")
 sample = ds_primary["train"][0]
 print(sample["question"])
 print(sample["error_category"])
-sample["student_scratchwork"].show()  # Display the scratchwork image
+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}
+  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 4.0](https://creativecommons.org/licenses/by/4.0/) license.