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README.md
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---
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dtype: int32
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- name: teachers_trained
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dtype: int32
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splits:
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- name: train
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num_bytes: 108
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num_examples: 1
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download_size: 4917
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dataset_size: 108
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- config_name: lms_market_data
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features:
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- name: year
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dtype: int32
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- name: quarter
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dtype: string
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- name: platform_name
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dtype: string
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- name: market_share_percentage
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dtype: float32
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- name: total_institutions
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dtype: int32
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- name: total_users
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dtype: int64
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- name: growth_rate_annual
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dtype: float32
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- name: geographic_scope
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dtype: string
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- name: data_source
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dtype: string
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splits:
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- name: train
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num_bytes: 92
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num_examples: 1
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download_size: 4462
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dataset_size: 92
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- config_name: platform_profiles
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features:
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- name: platform_id
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dtype: string
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- name: platform_name
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dtype: string
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- name: vendor_company
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dtype: string
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- name: platform_type
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dtype: string
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- name: primary_subject_areas
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list: string
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- name: supported_grade_levels
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list: string
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- name: deployment_model
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dtype: string
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- name: pricing_model
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dtype: string
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- name: launch_year
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dtype: int32
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- name: last_updated
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dtype: string
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- name: platform_url
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dtype: string
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- name: description
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dtype: string
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list: string
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- name: accessibility_features
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list: string
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splits:
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- name: train
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num_bytes: 413
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num_examples: 1
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download_size: 8081
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dataset_size: 413
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- config_name: screen_time_studies
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features:
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- name: study_id
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dtype: string
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dtype: string
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- name: publication_date
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dtype: string
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- name: sample_size
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dtype: int32
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- name: min_age
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dtype: int32
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- name: max_age
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dtype: int32
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- name: grade_levels_studied
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list: string
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- name: average_daily_screen_time_minutes
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dtype: int32
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- name: educational_screen_time_percentage
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dtype: float32
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- name: recreational_screen_time_percentage
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dtype: float32
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- name: methodology
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dtype: string
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- name: data_source
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dtype: string
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splits:
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- name: train
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num_bytes: 224
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num_examples: 1
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download_size: 6619
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dataset_size: 224
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configs:
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- config_name: ai_tutoring_metrics
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data_files:
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- split: train
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path: ai_tutoring_metrics/train-*
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- config_name: lms_market_data
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data_files:
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- split: train
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path: lms_market_data/train-*
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- config_name: platform_profiles
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data_files:
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- split: train
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path: platform_profiles/train-*
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- config_name: screen_time_studies
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data_files:
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- split: train
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path: screen_time_studies/train-*
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---
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---
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license: mit
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task_categories:
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- tabular-classification
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- text-classification
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tags:
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- education
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- k12
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- edtech
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- digital-learning
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- lms
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- screen-time
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- ai-tutoring
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- educational-technology
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language:
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- en
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size_categories:
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- 1K<n<10K
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---
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# K-12 Digital Learning Platforms Usage Dataset
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## Dataset Description
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This comprehensive dataset provides detailed information about digital learning platform usage, effectiveness, and trends in K-12 education. It covers Learning Management Systems (LMS), educational applications, AI tutoring systems, screen time impacts, and educational technology tools.
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### Dataset Summary
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- **Total Records**: 4
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- **Platforms Tracked**: 1
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- **Research Studies**: 1
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- **Market Data Points**: 1
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- **Geographic Scope**: United States and Canada
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- **Time Period**: 2020-2025
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## Dataset Structure
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### Categories
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1. **Platform Profiles**: Detailed information about educational platforms including features, pricing, and technical specifications
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2. **LMS Market Data**: Market share statistics, adoption rates, and competitive analysis
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3. **Screen Time Studies**: Research on screen time usage patterns and educational impacts
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4. **AI Tutoring Metrics**: Performance data and usage statistics for AI-powered tutoring systems
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5. **EdTech Tools**: Rankings, effectiveness data, and user feedback for educational technology tools
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### Data Sources
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- LearnPlatform by Instructure reports
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- Common Sense Media research studies
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- NCES (National Center for Education Statistics)
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- Academic research publications
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- Platform vendor statistics and reports
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- Market research organizations
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## Usage Examples
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### Research Applications
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- Educational technology effectiveness studies
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- Policy development and decision-making
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- Academic research on digital learning trends
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- Market analysis and competitive intelligence
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### Practical Applications
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- District technology planning and procurement
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- Platform evaluation and selection
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- Teacher training and professional development
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- Budget planning and cost analysis
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## Data Quality
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### Validation
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- Multi-source cross-validation
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- Automated quality checks
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- Expert review and curation
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- Regular accuracy audits
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### Limitations
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- Self-reported data limitations in some studies
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- Varying data collection methodologies across sources
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- Geographic bias toward North American data
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- Temporal lag in some research studies
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## Citation
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```bibtex
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@dataset{k12_digital_platforms_2025,
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title={K-12 Digital Learning Platforms Usage Dataset},
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author={Robworks Software},
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organization={https://robworks.info},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/datasets/robworks-software/k12-digital-learning-platforms}
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}
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```
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## License
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This dataset is released under the MIT License. All data is collected from publicly available sources and processed to ensure privacy compliance.
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## Updates
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Dataset is updated monthly with new research studies, market data, and platform information.
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Last Updated: October 08, 2025
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