Data Collection Framework

• 1: Sensor and Data Source Inventory
• 1.1: Biological Sensors
• 1.2: Behavioral Sensors
• 1.3: Environmental Sensors
• 1.4: Digital Interaction Sources
• 1.5: Social-Emotional Data Sources
• 1.6: Extracurricular Data Sources
• 2: Sensor Prioritization
• 2.1: Essential Data Streams
• 2.2: Supplementary Data Streams
• 3: Sensor Specifications and Standards
• 3.1: Technical Specifications
• 3.2: Calibration and Maintenance Procedures
• 3.3: Data Formats and Naming Conventions

1 - Sensor and Data Source Inventory

The Real Merit Protocol relies on a network of sensors and data streams to capture a student’s learning context, including physiological, behavioral, environmental, digital, and extracurricular factors. Continuous observation provides deeper insight into how a learner’s surroundings intersect with cognitive and emotional states.

1.1 - Biological Sensors

• Heart Rate Monitors: Capture heart rate data to gauge stress, engagement, or physiological alertness.

• Blood Oxygen Sensors: Measure oxygen saturation for insights into health and wellness.

• Temperature Sensors: Track body temperature to highlight stress or sleep quality factors.

• EDA Sensors: Monitor skin conductivity for emotional arousal or stress cues.

• EEG Sensors: Record brainwave patterns using non-invasive headsets, clarifying how learners focus and process information.

1.2 - Behavioral Sensors

• Cameras: Analyze posture, gestures, and micro-expressions to identify engagement or confusion.
• Microphones: Record speech elements such as tone, speed, or volume.
• Eye-Tracking Devices: Trace visual attention on learning materials or instructor displays.
• Touchscreens and Keyboards: Log typing speed, error rates, or usage patterns.
• Wearable Devices: Collect aggregated data like physical activity or movement, adding contextual support to a classroom profile.

1.3 - Environmental Sensors

• GPS Trackers: Link learning performance to specific locations.

• Ambient Light Sensors: Identify lighting conditions that may impact studying or focus.

• Noise Level Sensors: Determine how sound disruptions affect concentration.

• Air Quality Sensors: Assess CO₂ or particulate levels that might influence cognition.

1.4 - Digital Interaction Sources

• Learning Management Systems (LMS): Record login times, assignment submissions, and content usage patterns.

• Educational Software Usage: Monitor time spent on practice platforms to detect strengths or weaknesses.

1.5 - Social-Emotional Data Sources

• Emotional Assessments: Gather subjective ratings or surveys on well-being, mindset, or emotional states.

• Collaboration Data: Observe group project interactions or peer feedback to evaluate teamwork and communication.

1.6 - Extracurricular Data Sources

• Activity Records: Document involvement in sports, clubs, volunteer work, or leadership roles.

• Achievement Logs: Track recognitions, competitions, or community accomplishments.

2 - Sensor Prioritization

To optimize system performance and insights, data sources are categorized by priority.

2.1 - Essential Data Streams

• Behavioral Data: Real-time attention metrics from cameras, eye-tracking, and LMS logs.

• Biological Data: Heart rate, temperature, EEG for high-value insights into learner engagement.

2.2 - Supplementary Data Streams

• Environmental Data: Noise levels, air quality, and ambient light.

• Social-Emotional Data: Collaboration metrics and emotional self-assessments.

• Extracurricular Data: Participation and achievements outside the classroom.

3 - Sensor Specifications and Standards

3.1 - Technical Specifications

• Accuracy: Must capture sufficient resolution to detect meaningful shifts.

• Compatibility: Integrate with mainstream devices such as PCs, tablets, and smartphones.

• Connectivity: Permit USB, Bluetooth, or Wi-Fi connections.

• Durability: Support reliable operation under normal usage conditions.

3.2 - Calibration and Maintenance Procedures

• Initial Setup: Provide user-friendly calibration instructions to non-technical staff.

• Periodic Maintenance: Schedule routine recalibration and firmware updates.

• Error Detection: Alert users if sensor data falls below acceptable accuracy thresholds.

• Support Services: Offer remote troubleshooting or on-site service when necessary.

3.3 - Data Formats and Naming Conventions

• Standardization:

• CSV for numerical data.

• JSON for structured metadata.

• MP4 or WAV for multimedia.

• Naming Protocol: Use user IDs, sensor tags, and timestamps (Example: HeartRate_User12_20250108_0900.csv).

• Metadata Inclusion: Log calibration details, device serial numbers, or location for full documentation.