We believe that the pursuit of knowledge is a valuable and important aspect of education, and it should be carried out with integrity and honesty. Cheating not only undermines the learning process but also diminishes the value of hard work and effort put in by those who strive for academic excellence. We wanted to use new AI capabilities to help the teachers grade work more objectively.
Cheat Checker is a web-based application that uses computer vision to process the submitted videos from the student during the test-taking. Python model is trained to detect when the student is not looking at the screen and when their gaze is not stable. Our machine learning model detects when student is not looking at the screen, absent, and puts out the timestamps for that activities. Teachers can see for themselves the video and check with the provided data to make a fair judgment.
- Modified pretrained eye tracking model (by @antoinelame Python 2, 3 library), Pytorch.
- Django framework to connect back-end and front-end, HTML/CSS to write the front-end.
- Figma and Canva for designing.
- After uploading the video to the webpage, the detection of eye movements will be calculated and returned to you. By looking on the timestemps and checking them out in the player, Professor can make fair judjemnt and decide whether the student was cheating as wel as getting the data about their test taking.
- Gaze Tracker, Python libraries - https://github.com/antoinelame/GazeTracking
- Deep learning project - https://github.com/natanielruiz/deep-head-pose
- Wesley Johanson \ https://www.linkedin.com/in/wesleyzero/
- Aiturgan Talant \ https://www.linkedin.com/in/aiturgan-talant-8aa003250/
- Dalia Sebat \ http://linkedin.com/in/dalia-sebat-878ba1251/
- Lucy Ding \ https://www.linkedin.com/in/lucy-ding-40543918b/
