Due to the worldwide pandemic education sector is most one of the most affected sector in this situation online learning is the only hope. In these days online learning has emerged as one of the leading ways to transmit education and the government is looking for ways to shift education to online platforms due to the pandemic situation.It becomes difficult for the administration like schools, colleges,etc to have an unbiased feedback of the students for the faculty.

What it does

Our solution ie SMART CLASS Application helps professors better interact with those in their class and track their students' comprehension of the material with numerous ways to collect more data about classroom engagement. i.e. Total number of hands raised on a particular question, class attendance scheduling at specific time, attention analyzer of the students, and feedback of the students by face recognition.

Our Solution SMART CLASS bot will join the online meeting on ZOOM and collect the information from the browser client in the background of the host's computer. And will analyze the behaviour of the students/members and with the power of Smart Class App, teachers can also write/ draw in air and will be shown on the screen and will be live on the other student’s screen.

How we built it

  1. The data gathered using our python + selenium component is fed into our python + tkinter interface that is displayed on the host's computer, alongside their Zoom client.

  2. We built a bot using python and selenium to join the call (headless-ly) and collect all the information from the browser client in the background of the host's computer.

  3. Note taking feature using web-speech-api.

  4. Used CanvasJS for graph attentive analysis.

Challenges we ran into

  1. Zoom has no API for accessing a lot of the features we wanted to use, like the number of people raising their hands, the ability to send messages, the ability to get current users, etc.

2.While we had success with actually doing recognition of facial expressions, but making machine learning model that is accurate was tough task.

Accomplishments that we're proud of

Built a self-contained, fairly full-featured client to interface with the Zoom client headless-ly and providing some features which are not provided by Zoom.

What we learned

Throughout the hackathon we learn to deal with API and use them in proper way and using Machine learning being unfamiliar with it.

What's next for SMART CLASS

  1. Feedback Expression Analyzer which uses face recognition and gives the automated feedback of the students.
  2. Creating more accessible online classroom with its closed captioning service. This allows users with limited hearing to follow along more closely which improves usability
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