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metadata
license: creativeml-openrail-m
language:
  - en
tags:
  - code
  - computerscience
  - education
  - k-12
  - k12
  - technology
  - tech
pretty_name: K-12 Computer Science Curriculum Standards Dataset
size_categories:
  - n<1K

🚀 K-12 Computer Science Comprehensive Standards Dataset

📊 Dataset Overview

The most comprehensive K-12 computer science education dataset available, containing 696 learning standards spanning traditional CS concepts and cutting-edge areas including AI/ML, cybersecurity, data science, and robotics. This dataset aggregates and structures educational standards from authoritative sources to support curriculum development, educational research, and AI applications in computer science education.

🎯 Key Features

  • 📚 Comprehensive Coverage: 696 standards across 5 major CS areas
  • 🎓 Grade Progressive: Age-appropriate learning objectives K-12
  • 🏛️ Standards Aligned: Based on CSTA 2017 and ISTE 2024 frameworks
  • 🌍 Real-World Connected: Links to industry applications and workforce needs
  • 🔬 Research Ready: Structured for educational AI and learning analytics
  • 🎯 Assessment Ready: Complete with cognitive levels and evaluation frameworks

📈 Dataset Statistics

Metric Value Description
Total Standards 696 Complete learning objectives
Training Examples 556 (80%) For model training
Test Examples 140 (20%) For evaluation
Grade Levels 4 bands K-2, 3-5, 6-8, 9-12
CS Concepts 10 areas Traditional + emerging technologies
Programming Languages 15 types Age-appropriate progression
Subconcepts 83 topics Detailed subject breakdown

📚 Content Breakdown

Core Areas Covered

Area Standards Grade Range Focus
🖥️ Computing Systems 36 K-12 Hardware, software, troubleshooting
🌐 Networks & Internet 36 K-12 Cybersecurity, protocols, communication
📊 Data & Analysis 48 K-12 Collection, visualization, inference
⚙️ Algorithms & Programming 60 K-12 Computational thinking, coding
🌍 Impacts of Computing 36 K-12 Ethics, society, culture
💻 Programming Languages 225 K-12 ScratchJr → Java/Python/C++
🤖 Artificial Intelligence 65 K-12 Pattern recognition → deep learning
🔒 Cybersecurity 65 K-12 Password safety → penetration testing
📊 Data Science 65 K-12 Simple graphs → big data analytics
🤖 Robotics 60 K-12 Robot movement → AI robotics

Programming Language Progression

Elementary (K-2)

  • Visual Programming (ScratchJr)
  • Unplugged Activities
  • Basic sequencing and loops

Elementary (3-5)

  • Scratch programming
  • Hour of Code activities
  • Basic robotics programming

Middle School (6-8)

  • Python fundamentals
  • JavaScript basics
  • App development introduction
  • Web design basics

High School (9-12)

  • Java programming
  • C++ development
  • Advanced web development
  • Data science applications
  • AI/ML programming basics

🎓 Educational Framework Alignment

CSTA K-12 Computer Science Standards (2017)

Core Concepts Covered:

  1. Computing Systems - Hardware/software interactions, troubleshooting
  2. Networks and the Internet - Communication, cybersecurity, protocols
  3. Data and Analysis - Collection, organization, visualization, modeling
  4. Algorithms and Programming - Computational thinking, code development
  5. Impacts of Computing - Social, ethical, cultural implications

Computational Thinking Practices:

  • Fostering an Inclusive Computing Culture
  • Collaborating around Computing
  • Recognizing and Defining Computational Problems
  • Developing and Using Abstractions
  • Creating Computational Artifacts
  • Testing and Refining Computational Artifacts
  • Communicating about Computing

ISTE Computational Thinking Competencies (2024)

Educator Competencies Supported:

  • Computational Thinking (Learner) - Professional development goals
  • Equity Leader - Inclusive computing practices
  • Collaborating Around Computing - Cross-discipline integration
  • Creativity & Design - Human-centered design thinking
  • Integrating Computational Thinking - Cross-curricular applications

🛠️ Technical Implementation

Hardware/Platform Progression

K-2 (Ages 5-7)

  • Robots: Bee-Bot, Code & Go, KIBO
  • Tools: ScratchJr, unplugged activities
  • Focus: Sequencing, basic commands

3-5 (Ages 8-10)

  • Robots: LEGO Mindstorms, Sphero, Dash & Dot
  • Tools: Scratch, Hour of Code
  • Focus: Loops, conditionals, debugging

6-8 (Ages 11-13)

  • Platforms: Arduino, Raspberry Pi, VEX Robotics
  • Languages: Python, JavaScript
  • Focus: Functions, data structures, algorithms

9-12 (Ages 14-18)

  • Advanced: ROS, TensorFlow Lite, OpenCV
  • Languages: Java, C++, advanced Python
  • Focus: OOP, software engineering, AI/ML

Cybersecurity Tools by Grade

  • K-2: Password managers, basic digital safety
  • 3-5: Secure browsers, privacy settings
  • 6-8: Firewalls, VPNs, encryption basics
  • 9-12: Kali Linux, Metasploit, Nmap, penetration testing

📖 Dataset Structure

Schema

Each record contains the following fields:

{
  "standard_id": "CS.AI.912.DEEPLEARNING.1",
  "grade_level": "Grades 9-12",
  "concept": "Artificial Intelligence",
  "subconcept": "Deep Learning",
  "learning_objective": "Students will understand and apply deep learning in computing contexts",
  "computational_practices": ["Creating Computational Artifacts", "Testing and Refining"],
  "programming_language": "Python",
  "assessment_type": "Project",
  "cognitive_level": "Create"
}

Field Descriptions

  • standard_id: Unique identifier following CS.[AREA].[GRADE].[CONCEPT].[NUM] format
  • grade_level: Target grade range (Grades K-2, 3-5, 6-8, 9-12)
  • concept: Primary CS area (Computing Systems, AI, Cybersecurity, etc.)
  • subconcept: Specific topic within the concept area
  • learning_objective: Detailed description of what students should achieve
  • computational_practices: CSTA practices addressed by this standard
  • programming_language: Specific language used (when applicable)
  • assessment_type: Recommended evaluation method
  • cognitive_level: Bloom's taxonomy level (Remember, Understand, Apply, Analyze, Evaluate, Create)

Grade Level Distribution

Grade Band Examples Percentage Focus Areas
K-2 174 (25%) Early learners Foundational concepts, visual programming
3-5 174 (25%) Elementary Basic programming, digital citizenship
6-8 174 (25%) Middle school Intermediate programming, system thinking
9-12 174 (25%) High school Advanced concepts, career preparation

🎯 Use Cases and Applications

Educational Applications

Curriculum Development

  • Scope & Sequence Planning: Multi-year CS education pathways
  • Lesson Plan Generation: Age-appropriate activities for any CS topic
  • Assessment Creation: Comprehensive evaluation frameworks
  • Standards Alignment: Ensure curriculum meets national/state requirements

Teacher Professional Development

  • Training Programs: Structured learning paths for CS educators
  • Resource Planning: Hardware and software requirement planning
  • Best Practices: Evidence-based teaching strategies

Student Learning

  • Personalized Pathways: Adaptive learning based on student progress
  • Skill Assessment: Computational thinking evaluation tools
  • Portfolio Development: Project-based learning documentation

Research Applications

Educational Research

  • Learning Analytics: Analyze patterns in CS skill development
  • Curriculum Effectiveness: Evaluate different teaching approaches
  • Equity Studies: Research access and participation in CS education

AI/ML Applications

  • Content Generation: Train models to create educational materials
  • Assessment Automation: Develop automated evaluation tools
  • Recommendation Systems: Personalized learning recommendations
  • Natural Language Processing: Educational content analysis

Industry Applications

Workforce Development

  • Skills Gap Analysis: Identify industry training needs
  • Pipeline Planning: K-12 to career pathway development
  • Corporate Training: Employee upskilling programs

Product Development

  • EdTech Tools: Educational software and platform development
  • Assessment Platforms: Computational thinking evaluation systems
  • Learning Management: Curriculum management and tracking

🌍 Real-World Connections

Industry Alignment

Each standard connects to real-world applications and career pathways:

AI/Machine Learning

  • Applications: Netflix recommendations, autonomous vehicles, medical diagnosis
  • Careers: AI Engineer, Data Scientist, Machine Learning Researcher
  • Industry Growth: 22% projected growth through 2030

Cybersecurity

  • Critical Need: 600,000+ unfilled cybersecurity positions nationwide
  • Applications: Network security, threat detection, digital forensics
  • Careers: Security Analyst, Ethical Hacker, CISO

Data Science

  • Applications: Business analytics, scientific research, social media insights
  • Careers: Data Analyst, Business Intelligence, Research Scientist
  • Cross-Industry: Applicable in healthcare, finance, marketing, sports

Robotics

  • Applications: Manufacturing automation, healthcare assistance, space exploration
  • Careers: Robotics Engineer, Automation Specialist, AI Researcher
  • Emerging Areas: Service robots, collaborative robots, autonomous systems

Social Impact

Digital Equity

  • Inclusive Design: Standards emphasize accessibility and inclusion
  • Diverse Representation: Materials reflect diverse backgrounds and perspectives
  • Universal Access: Learning objectives designed for all students

Ethical Computing

  • AI Ethics: Age-appropriate discussions of bias, fairness, transparency
  • Digital Citizenship: Responsible technology use and online behavior
  • Privacy Awareness: Data protection and personal information security

📊 Data Quality and Validation

Source Validation

  • Authoritative Sources: Based on CSTA and ISTE official frameworks
  • Expert Review: Aligned with industry best practices
  • Educational Research: Grounded in learning science principles

Quality Metrics

  • Completeness: Comprehensive coverage across all grade levels
  • Consistency: Uniform structure and terminology
  • Accuracy: Technically accurate and pedagogically sound
  • Relevance: Current with 2024 industry needs and practices

Bias Considerations

  • Geographic: Based primarily on US educational standards
  • Cultural: May require adaptation for international contexts
  • Technological: Reflects current technology landscape (subject to change)
  • Economic: Assumes access to educational technology resources

🔄 Data Splits and Usage

Recommended Usage

Training Split (556 examples, 80%)

  • Model Training: Educational AI development
  • Curriculum Development: Standards-based course creation
  • Research Analysis: Pattern identification and trend analysis

Test Split (140 examples, 20%)

  • Model Evaluation: Performance assessment
  • Validation: Quality assurance for educational tools
  • Benchmarking: Comparison across different approaches

Reproducibility

  • Random State: 42 (ensures consistent splits)
  • Stratified Sampling: Maintains grade-level distribution
  • Version Control: Tracked changes and updates

🚀 Getting Started

Quick Start 

from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("robworks-software/k12-computer-science-comprehensive")

# Access training data
train_data = dataset["train"]
test_data = dataset["test"]

print(f"Training examples: {len(train_data)}")
print(f"Test examples: {len(test_data)}")
print(f"Features: {list(train_data.features.keys())}")

Filtering Examples 

# Filter by grade level
elementary = train_data.filter(
    lambda x: "K-2" in x["grade_level"] or "3-5" in x["grade_level"]
)

# Filter by subject area
ai_standards = train_data.filter(
    lambda x: x["concept"] == "Artificial Intelligence"
)

cybersecurity_standards = train_data.filter(
    lambda x: x["concept"] == "Cybersecurity"
)

programming_standards = train_data.filter(
    lambda x: x["programming_language"] != ""
)

Analysis Examples 

import pandas as pd
from collections import Counter

# Convert to pandas for analysis
df = train_data.to_pandas()

# Grade level distribution
grade_distribution = Counter(df["grade_level"])
print("Grade Level Distribution:", grade_distribution)

# Concept area breakdown
concept_distribution = Counter(df["concept"])
print("Concept Distribution:", concept_distribution)

# Cognitive level analysis
cognitive_distribution = Counter(df["cognitive_level"])
print("Cognitive Level Distribution:", cognitive_distribution)

# Programming language progression
prog_langs = df[df["programming_language"] != ""]["programming_language"]
print("Programming Languages:", Counter(prog_langs))

📄 Licensing and Attribution

License

This dataset is released under CC0 1.0 Universal (Public Domain Dedication).

You are free to:

  • Use the dataset for any purpose
  • Modify and adapt the content
  • Distribute copies and adaptations
  • Use commercially without restrictions

Attribution

While not required by the CC0 license, attribution is appreciated:

K-12 Computer Science Comprehensive Standards Dataset
Compiled by Ryan Robson, Robworks Software
Available at: https://huggingface.co/datasets/robworks-software/k12-computer-science-comprehensive

Source Attribution

This dataset aggregates and structures content from:

  • Computer Science Teachers Association (CSTA) - K-12 CS Standards 2017
  • International Society for Technology in Education (ISTE) - CT Competencies 2024
  • Various State Education Departments - Implementation guidelines
  • Industry Best Practices - Real-world applications and tools

📞 Contact and Support

Author Information

Repository

Support

For questions, issues, or collaboration opportunities:

  • Technical Support: support@robworks.info
  • Research Collaboration: Contact via website or email
  • Educational Partnerships: Open to working with schools and districts

📚 Citation

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

@dataset{robson2024k12cs,
  title={K-12 Computer Science Comprehensive Standards Dataset},
  author={Robson, Ryan},
  organization={Robworks Software},
  year={2024},
  publisher={HuggingFace},
  version={1.0.0},
  url={https://huggingface.co/datasets/robworks-software/k12-computer-science-comprehensive},
  note={Aggregated from CSTA 2017 and ISTE 2024 frameworks}
}

🤝 Contributing and Feedback

How to Contribute

While this dataset represents a comprehensive aggregation of existing standards, we welcome:

  • Error Reports: Corrections to technical inaccuracies
  • Enhancement Suggestions: Additional metadata or features
  • Application Examples: Use cases and implementations
  • Research Collaborations: Academic and industry partnerships

Roadmap

Potential future enhancements:

  • International Standards: Integration of non-US CS education frameworks
  • Assessment Rubrics: Detailed evaluation criteria for each standard
  • Learning Resources: Links to specific educational materials and tools
  • Career Pathways: Enhanced industry connection mapping
  • Multilingual Support: Translations for global accessibility

Community

Join the growing community of educators, researchers, and developers using this dataset:

  • Share your use cases and applications
  • Collaborate on educational tool development
  • Contribute to K-12 CS education research
  • Connect with others in the field

🌟 Impact Statement

This dataset represents a significant step forward in democratizing access to high-quality, standards-aligned computer science education resources. By providing a comprehensive, structured collection of K-12 CS learning objectives spanning traditional and emerging technology areas, we aim to:

  • Accelerate curriculum development and educational tool creation
  • Support teacher professional development and training
  • Enable research into effective CS education practices
  • Bridge the gap between education and industry workforce needs
  • Promote equity and inclusion in computer science education

Together, we can ensure that all students have access to world-class computer science education that prepares them for success in our increasingly digital world.

🌟 Star this dataset if it's useful for your work! 🔗 Share with educators and researchers in your network! 📧 Contact us for collaboration opportunities!