Education Edge IoT Dataset - Smart Classroom and Campus Monitoring

Dataset Overview

The Education Edge IoT Dataset published in July 2025 represents a pioneering effort to apply IoT technology and edge computing to educational environments. It captures comprehensive data from smart classrooms and campus facilities, enabling research into student behavior analysis, resource optimization, and data-driven educational improvements.

Data Collection Sources

Smart Classroom Sensors

• Attendance Tracking: RFID or BLE-based student check-in systems with precise timestamps
• Seat Occupancy: Pressure sensors or vision-based detection monitoring classroom utilization rates
• Environmental Quality: Temperature, humidity, CO2 levels, and illumination in learning spaces
• Noise Monitoring: Acoustic sensors measuring classroom ambient sound levels

Campus Infrastructure IoT

• Library Usage: Entry/exit sensors and desk occupancy tracking
• Laboratory Equipment: Usage logs from connected scientific instruments and computers
• Facility Access: Door sensors monitoring building and room entry patterns
• Energy Systems: Lighting and HVAC usage correlated with occupancy

Student Activity Tracking

• WiFi Analytics: Anonymous device presence detection (privacy-preserving)
• Cafeteria Traffic: Meal time patterns and facility utilization
• Transportation: Campus shuttle usage and parking lot occupancy

Behavioral Insights

The dataset enables analysis of:

• Study Patterns: When and where students prefer to study (library vs classroom vs outdoor)
• Attendance Correlation: Relationship between environmental conditions and class attendance
• Resource Utilization: Peak usage times for facilities requiring capacity planning
• Social Dynamics: Group formation patterns and collaborative learning spaces

Edge Computing Features

Designed specifically for edge analytics deployment:

• Lightweight Features: Optimized for processing on edge gateways with limited resources
• Real-Time Processing: Low-latency analytics for immediate feedback
• Privacy-Preserving: Aggregated metrics without individual identification
• Bandwidth Efficiency: Reduced cloud transmission through edge preprocessing

Educational Research Applications

Learning Environment Optimization

• Identify optimal classroom conditions (temperature, CO2, lighting) correlating with better attendance and engagement
• Design evidence-based facility improvements

Resource Planning

• Predict classroom and facility demand for efficient scheduling
• Optimize energy consumption based on actual occupancy patterns
• Plan library and study space expansions using utilization data

Student Support Services

• Detect students with irregular attendance patterns for early intervention
• Identify underutilized resources and promote awareness
• Improve campus safety through anomaly detection in access patterns

Machine Learning Tasks

• Classification: Predict classroom occupancy levels (empty/low/medium/full)
• Time-Series Forecasting: Anticipate facility demand for next day/week
• Clustering: Identify student behavior segments for personalized services
• Anomaly Detection: Detect unusual patterns indicating issues or emergencies

Smart Campus Innovation

This dataset supports the emerging smart campus movement, applying Industry 4.0 principles to education. It enables development of intelligent systems that adapt to student needs in real-time, improving learning outcomes while optimizing operational efficiency.