Inspiration

During this pandemic, we have all been put into circumstances we have never been in before. As students, it was important that we continued the high school education as smoothly as possible despite the restrictions that were in place. We created TeachAssist in order to make it easier for teachers to help students during online learning. When we say the prompt mention< "people who have influenced the minds of many," we immediately thought about all the teachers that have helped us from elementary school to high school. Teachers have had a huge impact on people across the world teaching us about math, science, or life. TeachAssist was created with those teachers in mind, the people that have had an impact on all us. With this project we will be standing on the shoulders of the giants that shape the world's young(or the future).

What it does

There are multiple facets of TeachAssist. The first is the Attention Tracker feature which allows teachers to check the attentiveness of students regardless of whether they are close or further away from the screen. Then the program will identify if the student is paying attention, and if the student isn't paying attention, an audio file will be played in order to get the student's attention. The second part is similar to a chatbot, where the teacher can input a command that will provide the instructions for the beginning of class, allowing the teacher to set up all the necessary materials and not have to worry about potentially forgetting the instructions.

How we built it

We used ML models through CV2 in order to get the facial mapping feature that could determine if the student was paying attention.

Challenges we ran into

The main challenges we ran into was fixing the audio output and creating a chatbot system through zoom, which required a lot of parameters.

Accomplishments that we're proud of

We're proud of the project we started that can truly help the people that have had such a positive impact on our lives. We are also proud that we were able to effectively train the model and create an effective system in a 12 hour hacking period.

What we learned

We learned even more about python in this hackathon, but also we learned how to create and develop our own idea within a much smaller hacking period. This hackathon gave us that rush to create and present our idea.

What's next for TeachAssist

With TeachAssist, we are planning on first improving our Zoom Chatbot and make it more versatile, and then we are already attempting to make our project more interactive by using EchoAR in order to add AR features to teacher presentations to increase student engagement. We hope that we can obtain data on usage and effectiveness to truly see how impactful our project is.

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