AI Learner Engagement Monitor
(AI-ENGAGE)
Learning Efficiency
Defined by the ability to apply course material, with a crucial factor being engagement between learners and teachers.
Engagement Traits:
Emotional: Students' feelings towards learning, environment, and people, shown through interest and facial expressions.
Behavioral: Attention and focus demonstrated by body language, such as hand gestures and eye movement.
Cognitive: Mental processing and connection-making, less observable and harder to measure.
Engagement Measurement
Can be assessed using sensors and computational methods.
Video-based Methodology
A non-invasive approach using Learner Processing Units (LPUs) to capture engagement through video and analyze it in real-time.
LPU Features
7” diameter, battery-powered, equipped with a camera and embedded computing for autonomous, scalable, and real-time data transmission.
Learning Biometric Sensor Network (LBSN)
An affordable and easy-to-use network of LPUs that provides actionable engagement data in various educational settings.
LBSN Benefits
Offers instructors and learners quantifiable engagement data to gauge the class's effectiveness, based on learners' ability to use taught concepts.
Algorithmic Foundation
Utilizes convolutional neural networks and deep learning for accurate engagement analysis, applicable in STEM, K-12, workshops, and training environments.
Our Prototype consists of a video-based learner processing unit, with Wi-Fi and a secure server.
AI-ENGAGE provides real-time assessment of learners’ engagement.
Video Demo
