Edu View is an AI-powered system designed to detect cheating in both offline and online exams using real-time monitoring with cameras, head movement tracking, and object recognition. This ensures a fair exam environment and improves the quality of assessments.
During exams, some student maybe doing cheating to get better results. But if keep doing that, it can impact the quality of student and their future. The teacher or exam overseer also cannot monitoring all student at once.
Edu View use AI system with camera monitoring to detect cheating behavior in real-time. The system helps teacher to identify cheating attempts and prevent unfair advantage, making students more prepared before exams.
Online exams make it hard to ensure student not doing cheating because teacher can't see directly what they doing. Student can easily look at notes, search internet, or get help without being noticed.
Edu View track head movement and detect objects around student during online exam. It can detect if student looking away from screen too much or if suspicious objects like books, phones, or other people appear nearby. Using YOLO for object detection and special algorithm for head tracking, teacher can focus more on exam instead of cheating concerns.
Some student develop silent cheat method like moving only their eyes to look at hidden notes, whispering answer to microphone, or secret mouth movements. Current monitoring system usually can't catch these subtle behaviors.
Edu View AI system focus on eye and mouth activity to detect silent cheating. It track suspicious eye movements like looking down or to side repeatedly, unusual mouth movements like whispering, and can detect if student speaking quietly. This make online exams more fair for everyone.
- Framework: Reflex (Frontend & Backend)
- Computer Vision: YOLO for object detection
- Facial Recognition: Mediapipe
- Deep Learning: Convolutional Neural Networks (CNN)
- Clone this repository:
git clone -b model2-testing https://github.com/Laoode/EduView.git cd eduview - Set up a virtual environment:
- For Windows users:
python -m venv venv . venv/Scripts/activate- For macOS and Linux users:
python3 -m venv venv source venv/bin/activate - Install dependencies:
pip install -r requirements.txt
- Run the application:
reflex run
- Open browser and access:
http://localhost:3000
This project is licensed under the MIT License.
If you have any questions or feedback, feel free to reach out!