Add comprehensive documentation with author info, contact details, and enhanced metadata

README.md

@@ -1,4 +1,47 @@
 ---
+license: cc0-1.0
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-classification
+- educational-assessment
+- text-generation
+task_ids:
+- educational-standards-alignment
+- computational-thinking-assessment
+- programming-education
+- ai-ml-education
+- cybersecurity-education
+- data-science-education
+- robotics-education
+tags:
+- education
+- k12
+- computer-science
+- csta-standards
+- iste-competencies
+- computational-thinking
+- programming
+- artificial-intelligence
+- machine-learning
+- cybersecurity
+- data-science
+- robotics
+- technology-education
+- stem
+- curriculum
+- learning-objectives
+pretty_name: K-12 Computer Science Comprehensive Standards
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
 dataset_info:
   features:
   - name: standard_id
@@ -12,7 +55,7 @@ dataset_info:
   - name: learning_objective
     dtype: string
   - name: computational_practices
-    list: string
+    dtype: string
   - name: programming_language
     dtype: string
   - name: assessment_type
@@ -21,13 +64,13 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 126180
+    num_bytes: 137428
     num_examples: 556
   - name: test
-    num_bytes: 32278
+    num_bytes: 34357
     num_examples: 140
-  download_size: 30246
-  dataset_size: 158458
+  download_size: 49536
+  dataset_size: 171785
 configs:
 - config_name: default
   data_files:
@@ -36,3 +79,467 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# 🚀 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:
+
+```json
+{
+  "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
+
+```python
+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
+
+```python
+# 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
+
+```python
+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
+- **Name**: Ryan Robson
+- **Company**: Robworks Software
+- **Website**: [robworks.info](https://robworks.info)
+- **Email**: [support@robworks.info](mailto:support@robworks.info)
+
+### Repository
+- **Dataset Repository**: [HuggingFace Dataset](https://huggingface.co/datasets/robworks-software/k12-computer-science-comprehensive)
+- **Source Code**: Available upon request
+
+### Support
+For questions, issues, or collaboration opportunities:
+- **Technical Support**: [support@robworks.info](mailto: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:
+
+```bibtex
+@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!