Student-Sight

GIF
What the computer sees
Built with Scalability in mind
How it works

Inspiration

COVID-19 has forced all schools and colleges to shut down, and as a result all the teachers are hosting classes online. It is nearly impossible for a teacher to make sure that every student is paying attention to the class. For example, the student could just join the class and physically leave to to do something else or use his smartphone. This is where Student-Sight comes in, it uses Computer Vision to keep the teacher notified about the amount of time that the student was actually paying attention to the ongoing class via telegram.

What it does

Our solution uses a Computer Vision script that works in harmony with a Telegram Bot that notifies the teacher about the amount of time the student is actually spending in front of the laptop. The amount of time spent on and off-screen for each student is logged and sent to the teacher using the telegram bot.

How we built it

The project was divided into 2 main parts:

The computer vision pipeline uses the live webcam feed of the laptop and verified the physical presence of the student. It also logged the amount of time spent on and off screen.
The telegram bot sends 2 kinds of messages to the teacher:
1. When a student enters the class, it sends:
```
Spongebob {ID: hack101} just joined the class
```
2. When a student leaves the class, it sends:
```
Spongebob {ID: hack101} left the class 
Uptime: 00:25:46 
Downtime: 0:10:14
```
  Note: The name and ID for each student can be changed by simply changing the students.json file.

Scalable out of the box

Our system can be easily used by multiple students at the same session reporting to a single teacher.

Challenges we ran into

This was the first time we were building a telegram bot, it was a bit challenging at first, but we figured out our way through it thanks to the documentation.

Accomplishments that we're proud of

This was the first time that we were trying to deploy a computer vision pipeline on a platform like telegram. It also took us quite some time to figure out how to properly build the computer vision pipeline.

What we learned

We learnt how to build a telegram bot, and how to integrate it with other scripts to send messages in real time to the user. We also learned how to efficiently detect faces and eyes of any user using the live webcam feed using openCV.