img img img img img
img

Emotion Recognition for Student Engagement

Objective:

To develop an AI-powered system that analyzes students’ facial expressions and emotions during online classes, providing educators with insights into engagement, attention, and emotional state in real time.

Why Choose This Project:

  • Helps educators assess student engagement during virtual lectures.

  • Identifies students who may need additional attention or support.

  • Integrates computer vision, AI, and real-time analytics, which are highly relevant for modern EdTech solutions.

  • Supports data-driven improvement of teaching methods.

Key Features:

Feature Description
Real-Time Emotion Detection Detects emotions such as happy, sad, neutral, surprised, or confused from webcam feeds.
Engagement Analytics Provides metrics on attention span, emotional trends, and participation.
Dashboard Visualization Live and historical engagement graphs for teachers.
Alert System Notifies educators if multiple students show disengagement or negative emotions.
Class Summary Reports Generates end-of-class reports summarizing emotional patterns.
Multi-Student Monitoring Tracks engagement for all students in the virtual classroom simultaneously.

Technology Stack:

  • Frontend: HTML, CSS, JavaScript, React.js (for dashboard).

  • Backend: Python (Flask / Django) or Node.js (Express).

  • AI & Computer Vision: OpenCV, Dlib, TensorFlow/Keras, DeepFace or FER libraries.

  • Database: MongoDB / MySQL for storing engagement data and reports.

  • Cloud (Optional): AWS / Azure / GCP for hosting, processing, and scaling.

Working Flow:

  1. User Authentication

    • Students and teachers log in securely to the online class platform.

  2. Video Feed Capture

    • Webcam feed from each student is captured in real-time.

  3. Emotion Detection & Analysis

    • Facial expressions are analyzed using AI models to detect emotions.

    • Engagement score is calculated based on emotional states over time.

  4. Real-Time Dashboard Updates

    • Teachers view live analytics showing engagement levels and emotional trends.

  5. Alerts & Notifications

    • Alerts are triggered if students show signs of disengagement or confusion.

  6. Reporting

    • End-of-class summary reports include statistics on engagement, attention, and emotional patterns.

Main Modules:

  1. User Authentication & Management Module

  2. Video Capture Module

  3. Emotion Detection & Processing Module

  4. Engagement Analytics Module

  5. Dashboard & Visualization Module

  6. Reporting & Notification Module

Security Features:

  • Secure authentication for students and teachers.

  • Data encryption for webcam feeds and engagement metrics.

  • Privacy compliance for storing and processing student facial data.

  • Role-based access to dashboards and reports.

This Course Fee:

₹ 1899 /-

Project includes:
  • Customization Icon Customization Fully
  • Security Icon Security High
  • Speed Icon Performance Fast
  • Updates Icon Future Updates Free
  • Users Icon Total Buyers 500+
  • Support Icon Support Lifetime
Secure Payment:
img
Share this course: