Problem Statement
Over the last year, students have been facing many problems with online learning, and some of those problems increased when hybrid learning began. The problem we are trying to solve, is how to bridge the gap between the lack of interaction in online learning, with the interactive, fun environment in a real classroom. To allow for remote learning to be the same as in person, we use a machine learning algorithm that can measure the student’s engagement in the subject. One of the biggest issues with the remote learning is that there are many distractions for students that can take their attention away from the lessons and cause them to fall behind in class. Our idea will measure the class’s engagement and allow the teacher to understand what works and what doesn’t in the online class compared to in-person.
Inspiration
Our major inspiration to do this was the problem we faced personally on our learning during this pandemic. Our team consists of participants from India and the US. Irrespective of the country, we shared the common issues over the same. So being students we had to think for ourselves and created this idea.
What it does
Real Class is beneficial for all types of distance education. The platform can be used by any educational institution as a tool for that enables the educators and tutors to record themselves teaching every day. Meanwhile, the student can watch the video anytime between the stipulated period of time, to get their attendance. There can be a minimum percentage of attendance set by the institution to pass or qualify for a subject or course. When a student starts to watch a video, the system uses their camera to track their attention. This is the place where our Machine Learning comes into role. We use the ML model to trace their engagement and attention. This can be achieved by monitoring the student's facial expression, behavior, actions, eyeball movement, and response to the video. To get check their attention and make it more interactive, we add a chatbox, and the tutors can ask questions in between the lessons. This chatbox enables us to get further details about engagement according to response time and response accuracy. Once the student watches the video, a report is sent to Institution or the faculty which contains the attention & engagement analysis and answers made by the student for the questions.
How we built it
The model we built as a prototype of our entire idea. A huge thanks to our mentor Mykola Zaitsev for guidance. He introduced us to OpenCV using which we built the model. We also used several other frameworks which are mentioned in the "Built with" column
Challenges we ran into
As this was our first hackathon, it was challenging to work with team members present in different geographic locations for full 24 hours in the hackathon environment. For us Machine learning, OpenCV-Python library, and Python-Ecosystem environment was new, we learned from online tutorials/articles and implemented this cutting-edge technology into building our prototype.
Accomplishments that we're proud of
During this hackathon, we have gained skills in both technical and soft skills. By creating a working prototype that will help with future generations to make better learning environment, we were able to improve our coding skills, use new softwares and technologies. Additionally by working with team members from different geographic regions, we were able to improve our communication skills, team building and goal setting.
What we learned
During the 3month period before the start of the hackathon we were able to become familiar with the concepts Machine Learning and OpenCV-Python frameworks and libraries. Then, using the skills learned, we presented the prototype of our working model: Real Class, during the span of the 24 hour hackathon.
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