Datasets:

robworks-software
/

k12-science-standards

license: mit
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

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

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

# Get middle school examples (grades 6-8)
middle_school = train_df[train_df['metadata_grade_level'].isin(['6', '7', '8'])]

Filter by Domain

# Get Life Sciences examples
life_sciences = train_df[train_df['metadata_domain'] == 'Life Sciences']

Filter by Complexity

# 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:

@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.

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.