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README.md

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+---
+license: cc-by-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
+---
+
+# ScratchMath
+
+**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.
+
+## Dataset Description
+
+The dataset contains 1,720 samples of authentic student scratchwork collected from an online education platform. Each sample includes:
+
+- 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
+
+### Subsets
+
+| Subset | Description | Samples |
+|--------|-------------|---------|
+| `primary` | Primary school (Grades 1–6) math problems | 1,479 |
+| `middle` | Middle school (Grades 7–9) math problems | 241 |
+
+### Error Categories
+
+Each sample is labeled with one of seven error categories:
+
+| Category | English | 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 |
+| 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 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
+
+```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()  # Display the scratchwork image
+```
+
+## 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 4.0](https://creativecommons.org/licenses/by/4.0/) license.