README.md

---
license: cc0-1.0
language:
- en
tags:
- education
- k-12
- science
- stem
- ngss
- assessment
- curriculum
- learning
- standards
- educational-ai
- three-dimensional-learning
- bloom-taxonomy
- depth-of-knowledge
- scientific-practices
- crosscutting-concepts
pretty_name: K-12 Science Standards Aligned Learning Framework
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- text-generation
- question-answering
task_ids:
- text2text-generation
- multi-class-classification
- open-domain-qa
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: input
    dtype: string
  - name: output
    dtype: string
  - name: task
    dtype: string
  - name: metadata_standard_code
    dtype: string
  - name: metadata_grade_level
    dtype: string
  - name: metadata_domain
    dtype: string
  - name: metadata_core_idea
    dtype: string
  - name: metadata_core_idea_title
    dtype: string
  - name: metadata_ngss_practice
    dtype: string
  - name: metadata_crosscutting_concept
    dtype: string
  - name: metadata_dok_level
    dtype: string
  - name: metadata_bloom_level
    dtype: string
  - name: metadata_complexity_level
    dtype: string
  - name: metadata_three_dimensional
    dtype: string
  - name: metadata_ngss_aligned
    dtype: string
  - name: metadata_assessment_type
    dtype: string
  - name: metadata_estimated_time
    dtype: string
  splits:
  - name: train
    num_bytes: 3778013
    num_examples: 4750
  - name: validation
    num_bytes: 808193
    num_examples: 1018
  - name: test
    num_bytes: 810805
    num_examples: 1019
  download_size: 202337
  dataset_size: 5397011
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# K-12 Science Standards Aligned Learning Framework Dataset

A comprehensive dataset of K-12 science curriculum standards aligned with the Next Generation Science Standards (NGSS), designed for training and evaluating educational AI systems.

## Dataset Overview

This dataset contains **6,787 examples** of educational content spanning all K-12 grade levels and science domains. Each example includes instructional content, student inputs, expected outputs, and rich metadata aligned with educational standards.

### Quick Stats
- **Total Examples**: 6,787 (Train: 4,750 | Validation: 1,018 | Test: 1,019)
- **Grade Coverage**: Kindergarten through Grade 12
- **Science Domains**: Life Sciences, Physical Sciences, Earth & Space Sciences, Engineering Design
- **Format**: Parquet files for efficient loading and processing

## Dataset Structure

### Core Fields
- **`instruction`**: Learning objective or task description
- **`input`**: Student prompt or context information
- **`output`**: Expected response or assessment criteria
- **`task`**: Type of scientific thinking skill required

### Educational Metadata
- **`metadata_standard_code`**: NGSS standard identifier (e.g., "MS-LS3-5")
- **`metadata_grade_level`**: Grade level (K, 1-12)
- **`metadata_domain`**: Science domain
- **`metadata_core_idea`**: NGSS disciplinary core idea
- **`metadata_ngss_practice`**: Science and engineering practice
- **`metadata_crosscutting_concept`**: NGSS crosscutting concept

### Assessment Metadata
- **`metadata_dok_level`**: Depth of Knowledge level (1-4)
- **`metadata_bloom_level`**: Bloom's taxonomy level
- **`metadata_complexity_level`**: Learning complexity assessment
- **`metadata_assessment_type`**: Type of assessment activity
- **`metadata_estimated_time`**: Estimated completion time (minutes)
- **`metadata_three_dimensional`**: Three-dimensional learning indicator
- **`metadata_ngss_aligned`**: NGSS alignment verification

## Content Categories

### Grade Levels
Kindergarten through Grade 12, providing comprehensive coverage across all K-12 educational levels.

### Science Domains
- **Life Sciences**: Biology, ecology, heredity, evolution
- **Physical Sciences**: Chemistry, physics, energy, matter
- **Earth and Space Sciences**: Geology, astronomy, climate, natural resources
- **Engineering Design**: Design thinking, problem-solving, technological solutions

### Task Types
- **Data Analysis**: Interpreting scientific data and evidence
- **Evidence Evaluation**: Assessing the validity of scientific claims
- **Experimental Design**: Planning and designing investigations
- **Scientific Inquiry**: Asking questions and forming hypotheses
- **Scientific Explanation**: Constructing evidence-based explanations
- **Model Construction**: Building and using scientific models
- **Engineering Design**: Solving problems through design processes
- **Hypothesis Formation**: Developing testable predictions

### Assessment Types
- Argument Construction
- Model Building
- Engineering Design Challenges
- Computational Modeling
- Lab Investigations
- Data Analysis Tasks
- Scientific Argumentation
- Research Projects
- Observation Tasks
- Hands-on Investigations

## Usage

### Loading the Dataset

#### Using Pandas
```python
import pandas as pd

# Load individual splits
train_df = pd.read_parquet('data/train-00000-of-00001.parquet')
val_df = pd.read_parquet('data/validation-00000-of-00001.parquet')
test_df = pd.read_parquet('data/test-00000-of-00001.parquet')

# Load all data
all_df = pd.concat([train_df, val_df, test_df], ignore_index=True)
```

#### Using HuggingFace Datasets
```python
from datasets import load_dataset

# Load from local directory
dataset = load_dataset('parquet', data_dir='data/')

# Access splits
train_dataset = dataset['train']
validation_dataset = dataset['validation']
test_dataset = dataset['test']
```

### Example Usage Patterns

#### Filter by Grade Level
```python
# Get middle school examples (grades 6-8)
middle_school = train_df[train_df['metadata_grade_level'].isin(['6', '7', '8'])]
```

#### Filter by Domain
```python
# Get Life Sciences examples
life_sciences = train_df[train_df['metadata_domain'] == 'Life Sciences']
```

#### Filter by Complexity
```python
# Get proficient-level assessments
proficient = train_df[train_df['metadata_complexity_level'] == 'Proficient']
```

## Educational Standards Alignment

This dataset is meticulously aligned with:

- **Next Generation Science Standards (NGSS)**: All content maps to specific NGSS performance expectations
- **Three-Dimensional Learning**: Integrates disciplinary core ideas, crosscutting concepts, and science practices
- **Depth of Knowledge (DOK)**: Content is categorized by cognitive complexity levels
- **Bloom's Taxonomy**: Learning objectives are classified by cognitive processes

## Applications

This dataset is designed for:

- **Educational AI Training**: Developing AI tutors and assessment systems
- **Curriculum Development**: Creating standards-aligned educational content
- **Assessment Research**: Studying educational measurement and evaluation
- **Learning Analytics**: Analyzing student learning patterns and outcomes
- **Teacher Professional Development**: Training educators on standards implementation

## Data Quality

- **Standards Verification**: All content verified against official NGSS documentation
- **Educational Review**: Content reviewed by certified science educators
- **Cognitive Alignment**: DOK and Bloom's levels validated by assessment experts
- **Three-Dimensional Integration**: Ensures authentic scientific learning experiences

## Ethical Considerations

- Content is designed to be inclusive and culturally responsive
- Assessment examples avoid bias and promote equity in science education
- All content supports diverse learners and learning styles
- Aligned with educational best practices for K-12 science instruction

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{k12_science_standards_2024,
  title={K-12 Science Standards Aligned Learning Framework Dataset},
  author={[Author Information]},
  year={2024},
  publisher={[Publisher Information]},
  url={[Repository URL]}
}
```

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

## Contributing

We welcome contributions to improve the dataset quality and coverage. Please see our contribution guidelines for more information.

---

*This dataset supports the development of AI systems that can provide high-quality, standards-aligned science education for all K-12 students.*