---
license: cc-by-nc-3.0
task_categories:
- question-answering
- visual-question-answering
- image-to-text
language:
- en
tags:
- science
- education
- textbook
- multimodal
- ck12
- middle-school
- diagrams
- vision-language
pretty_name: CK-12 TQA Multimodal - Textbook Question Answering with Images
size_categories:
- 10K<n<100K
---

# CK-12 TQA Multimodal: Textbook Question Answering with Images

## Dataset Description

### Dataset Summary

**CK-12 TQA Multimodal** is a comprehensive multimodal dataset for science education, containing **26,260 questions** paired with **6,206 images** from middle school science textbooks. This dataset is sourced from [CK-12 Foundation](https://www.ck12.org/)'s open educational resources and includes both text-only questions and diagram-based visual reasoning questions.

This is the **complete multimodal version** with all images properly organized and mapped to questions, ready for training vision-language models, visual question answering systems, and multimodal educational AI.

**Dataset Highlights:**
- 🖼️ **6,206 images** across 4 types (question diagrams, labeled diagrams, teaching images, textbook figures)
- 📊 **26,260 questions** (12,567 require diagrams, 13,693 are text-only)
- 📚 **1,076 lessons** from Life Science, Earth Science, and Physical Science
- 🎯 Proper train/validation/test splits with images organized by split
- 🔬 Middle school level (grades 6-8) science content
- ✅ Images verified and mapped to questions with metadata

### Original Source

- **Paper**: [Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension](https://allenai.org/data/tqa) (CVPR 2017)
- **Authors**: Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, Hannaneh Hajishirzi
- **Content**: [CK-12 Foundation](https://www.ck12.org/) open-source science textbooks
- **License**: CC BY-NC 3.0

### Supported Tasks

- **Visual Question Answering (VQA)**: Answer questions about scientific diagrams
- **Multimodal Reasoning**: Combine text and visual information
- **Question Answering**: Text-based comprehension
- **Science Education**: Middle school science assessment
- **Diagram Understanding**: Interpret charts, graphs, and scientific illustrations

## Dataset Structure

### Data Organization

```
ck12_tqa_multimodal/
├── ck12_tqa_train.jsonl          # 15,154 questions
├── ck12_tqa_validation.jsonl     # 5,309 questions
├── ck12_tqa_test.jsonl           # 5,797 questions
├── dataset_manifest.json         # Dataset statistics
└── images/
    ├── train/
    │   ├── question_images/      # 1,077 diagram question images
    │   ├── abc_question_images/  # 422 labeled diagram images
    │   ├── teaching_images/      # 185 instructional diagrams
    │   └── textbook_images/      # 1,972 textbook figures
    ├── validation/
    │   ├── question_images/      # 450 images
    │   ├── abc_question_images/  # 140 images
    │   ├── teaching_images/      # 60 images
    │   └── textbook_images/      # 593 images
    └── test/
        ├── question_images/      # 516 images
        ├── abc_question_images/  # 144 images
        ├── teaching_images/      # 31 images
        └── textbook_images/      # 616 images
```

### Data Format

Each question includes complete metadata and image mappings:

**Non-Diagram Question:**
```json
{
  "id": "NDQ_000046",
  "source": "TQA-CK12",
  "split": "train",
  "lesson_id": "L_0002",
  "lesson_name": "earth science and its branches",
  "has_diagram": false,
  "instruction": "Answer the following multiple choice question from a science textbook.",
  "input": "Earth science is the study of\n\nOptions:\na) solid Earth.\nb) Earths oceans.\nc) Earths atmosphere.\nd) all of the above",
  "output": "d",
  "question_type": "Multiple Choice",
  "question_subtype": "Multiple Choice",
  "options": ["solid Earth.", "Earths oceans.", "Earths atmosphere.", "all of the above"],
  "option_labels": ["a", "b", "c", "d"]
}
```

**Diagram Question (with image):**
```json
{
  "id": "DQ_000001",
  "source": "TQA-CK12",
  "split": "train",
  "lesson_id": "L_0003",
  "lesson_name": "erosion and deposition by flowing water",
  "has_diagram": true,
  "instruction": "Answer the following multiple choice question about the diagram from a science textbook.",
  "input": "How many actions are depicted in the diagram?\n\nOptions:\na) 6\nb) 4\nc) 8\nd) 7",
  "output": "d",
  "question_type": "Diagram Multiple Choice",
  "question_subtype": "",
  "image_path": "question_images/erosion_6843.png",
  "image_name": "erosion_6843.png",
  "options": ["6", "4", "8", "7"],
  "option_labels": ["a", "b", "c", "d"]
}
```

### Data Fields

- `id`: Unique identifier (DQ_* = diagram question, NDQ_* = non-diagram)
- `source`: "TQA-CK12"
- `split`: "train", "validation", or "test"
- `lesson_id`: Lesson identifier
- `lesson_name`: Topic name
- `has_diagram`: Boolean indicating if image is required
- `instruction`: Model instruction prompt
- `input`: Question text with formatted options
- `output`: Correct answer (letter: a, b, c, d, etc.)
- `question_type`: "Multiple Choice", "True/False", etc.
- `question_subtype`: Additional categorization
- `options`: List of answer choices
- `option_labels`: List of choice labels
- `image_path`: Relative path to image (for diagram questions)
- `image_name`: Image filename (for diagram questions)

### Image Types

#### 1. Question Images (`question_images/`)
- **Purpose**: Diagrams directly referenced in questions
- **Count**: 2,043 images total
- **Usage**: Required to answer diagram questions
- **Examples**: Scientific diagrams, charts, illustrations

#### 2. ABC Question Images (`abc_question_images/`)
- **Purpose**: Labeled versions of diagrams with letter markers
- **Count**: 706 images total
- **Usage**: Questions about specific labeled parts
- **Examples**: Diagrams with A, B, C, D labels

#### 3. Teaching Images (`teaching_images/`)
- **Purpose**: Instructional diagrams with detailed descriptions
- **Count**: 276 images total
- **Usage**: Supporting visual content
- **Examples**: Concept illustrations, process diagrams

#### 4. Textbook Images (`textbook_images/`)
- **Purpose**: Figures from textbook lessons
- **Count**: 3,181 images total
- **Usage**: Contextual illustrations
- **Examples**: Photos, drawings, charts from lessons

### Data Splits

| Split | Lessons | Questions | Non-Diagram | Diagram | Images |
|-------|---------|-----------|-------------|---------|--------|
| **Train** | 666 | 15,154 | 8,653 | 6,501 | 3,656 |
| **Validation** | 200 | 5,309 | 2,528 | 2,781 | 1,243 |
| **Test** | 210 | 5,797 | 2,512 | 3,285 | 1,307 |
| **TOTAL** | **1,076** | **26,260** | **13,693** | **12,567** | **6,206** |

**Split Strategy**: Splits are created at the **lesson level** to minimize concept overlap between train/val/test sets. Related lessons are grouped together to prevent data leakage.

## Usage

### Loading the Dataset

```python
from datasets import load_dataset
from PIL import Image
import json

# Load questions
dataset = load_dataset("notefill/ck12-tqa-multimodal", data_files="ck12_tqa_train.jsonl")

# Load a question with image
with open("ck12_tqa_train.jsonl", "r") as f:
    for line in f:
        q = json.loads(line)
        if q["has_diagram"]:
            # Construct image path
            img_path = f"images/{q['split']}/{q['image_path']}"
            # Load image
            image = Image.open(img_path)
            print(f"Question: {q['input']}")
            print(f"Answer: {q['output']}")
            image.show()
            break
```

### Filter by Image Type

```python
import json

# Load only diagram questions
diagram_questions = []
with open("ck12_tqa_train.jsonl", "r") as f:
    for line in f:
        q = json.loads(line)
        if q["has_diagram"]:
            diagram_questions.append(q)

print(f"Found {len(diagram_questions)} diagram questions")
```

### Vision-Language Model Training

```python
from transformers import VisionEncoderDecoderModel, AutoTokenizer, AutoFeatureExtractor
from PIL import Image
import json

# Load model (example with BLIP, ViLT, or similar)
model = VisionEncoderDecoderModel.from_pretrained("your-model")
tokenizer = AutoTokenizer.from_pretrained("your-model")
feature_extractor = AutoFeatureExtractor.from_pretrained("your-model")

# Prepare data
def prepare_multimodal_data(jsonl_file, image_dir):
    data = []
    with open(jsonl_file, "r") as f:
        for line in f:
            q = json.loads(line)
            if q["has_diagram"]:
                img_path = f"{image_dir}/{q['split']}/{q['image_path']}"
                image = Image.open(img_path).convert("RGB")
                data.append({
                    "image": image,
                    "question": q["input"],
                    "answer": q["output"],
                    "instruction": q["instruction"]
                })
    return data

train_data = prepare_multimodal_data("ck12_tqa_train.jsonl", "images")
```

### Evaluation

```python
def evaluate_vqa_model(model, test_data):
    """Evaluate visual question answering accuracy"""
    correct = 0
    total = 0
    
    for item in test_data:
        if item["has_diagram"]:
            # Load image
            img_path = f"images/{item['split']}/{item['image_path']}"
            image = Image.open(img_path)
            
            # Get prediction
            prediction = model.predict(image, item["input"])
            
            if prediction.strip().lower() == item["output"].strip().lower():
                correct += 1
            total += 1
    
    return correct / total if total > 0 else 0.0
```

## Subject Coverage

### Life Science (Biology)
- Cell structure and function
- Genetics and heredity
- Ecology and ecosystems
- Evolution and adaptation
- Human body systems
- Plants and animals

### Earth Science
- Geology and rock cycle
- Plate tectonics
- Weather and climate
- Oceans and water cycle
- Earth's atmosphere
- Astronomy and solar system

### Physical Science
- Matter and its properties
- Chemical reactions
- Forces and motion
- Energy forms and transfer
- Waves and sound
- Electricity and magnetism

## Benchmark Performance

This dataset is designed for challenging multimodal comprehension. Key difficulty factors:

1. **Visual Reasoning**: Requires interpreting scientific diagrams
2. **Multi-step Reasoning**: Many questions need multiple inferences
3. **Domain Knowledge**: Requires middle school science knowledge
4. **Diagram Complexity**: Varying levels of visual complexity

## Considerations for Using the Data

### Recommended Uses

✅ Training vision-language models for education  
✅ Evaluating multimodal reasoning capabilities  
✅ Building educational AI tutoring systems  
✅ Research in visual question answering  
✅ Developing diagram understanding systems  
✅ Science education assessment tools  

### Limitations

⚠️ **Grade Level**: Limited to middle school (ages 11-14)  
⚠️ **Subject Scope**: Only Life, Earth, and Physical Science  
⚠️ **Language**: English only  
⚠️ **Image Quality**: Varies by source  
⚠️ **Answer Format**: Multiple choice only  
⚠️ **Non-Commercial**: License restricts commercial use  

### Ethical Considerations

- Dataset designed for research and non-commercial educational purposes
- Content sourced from CK-12's open educational resources
- Should be used to improve educational access and equity
- Human oversight recommended for student-facing applications
- Consider diverse student needs when deploying AI systems

## Citation

### Original TQA Dataset

```bibtex
@inproceedings{Kembhavi2017tqa,
  title={Are You Smarter Than A Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension},
  author={Aniruddha Kembhavi and Minjoon Seo and Dustin Schwenk and Jonghyun Choi and Ali Farhadi and Hannaneh Hajishirzi},
  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017}
}
```

### This Multimodal Version

```bibtex
@dataset{ck12_tqa_multimodal2025,
  title={CK-12 TQA Multimodal: Textbook Question Answering with Images},
  author={Kuyeso Rogers and Adiza Alhassan and Notefill},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/notefill/ck12-tqa-multimodal}},
  note={Complete multimodal dataset with images from CK-12 textbooks}
}
```

## Licensing

**Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)**

### You are free to:
- ✅ **Share**: Copy and redistribute the material
- ✅ **Adapt**: Remix, transform, and build upon the material

### Under the following terms:
- 📝 **Attribution**: Credit CK-12 Foundation and TQA authors
- ❌ **NonCommercial**: No commercial use without permission

For commercial licensing, contact CK-12 Foundation.

## Additional Resources

- **Original TQA**: [allenai.org/data/tqa](https://allenai.org/data/tqa)
- **CK-12 Foundation**: [www.ck12.org](https://www.ck12.org/)
- **Paper (CVPR 2017)**: [TQA Paper](https://allenai.org/data/tqa)
- **Allen AI**: [allenai.org](https://allenai.org/)

## Acknowledgments

We gratefully acknowledge:

- **CK-12 Foundation** for creating and freely distributing world-class science educational materials under open licenses
- **Original TQA Authors**: Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, and Hannaneh Hajishirzi for their groundbreaking work on multimodal machine comprehension
- **Allen Institute for AI (AI2)** for hosting and maintaining the original dataset
- The educators and content creators who contributed to CK-12's science curriculum

### About CK-12 Foundation

CK-12 Foundation is a non-profit organization providing free, high-quality K-12 STEM content. Their mission is to reduce the cost of textbook materials and increase access to quality education worldwide through openly-licensed, customizable content.

## Dataset Statistics

```json
{
  "total_questions": 26260,
  "total_images": 6206,
  "total_lessons": 1076,
  "diagram_questions": 12567,
  "text_questions": 13693,
  "image_types": 4,
  "subjects": ["Life Science", "Earth Science", "Physical Science"],
  "grade_level": "Middle School (6-8)",
  "languages": ["English"]
}
```

## Contact

For questions about this multimodal version, please open an issue on the dataset repository.

For questions about the original TQA dataset, visit [allenai.org/data/tqa](https://allenai.org/data/tqa).

For CK-12 content and licensing inquiries, visit [www.ck12.org](https://www.ck12.org/).