Inspiration
The need for engaging and effective teaching inspired us to create ClassLens. We wanted to bridge the gap between educators and student attentiveness using AI to foster a better learning environment.
What it does
Currently, ClassLens analyzes classroom images to assess student attentiveness. It identifies the total number of students in the frame, calculates how many are attentive, and visualizes these insights through an attentiveness curve. This provides teachers with valuable feedback that helps them adjust their teaching methods to better engage students.
How we built it
We built ClassLens using: We developed ClassLens using a combination of modern technologies. The frontend was built with React.js, enabling an interactive and dynamic user interface. The backend was developed using Node.js to handle server-side logic. For the AI/ML components, we utilized YOLO for real-time object detection of students and their behaviors, PyTorch for behavior classification (e.g., raising hands or reading), OpenCV for preprocessing and image normalization, and FastAPI to integrate the backend with the machine learning models. The user interface was designed using Figma, ensuring a smooth and intuitive experience.
Challenges we ran into
We faced several challenges during the development process, especially with the limited time available. The tight hackathon schedule made it difficult to fully train and fine-tune the AI model. Additionally, we had to ensure consistent analysis despite variations in lighting and image quality. Lastly, integrating the AI model with the frontend interface in a smooth, seamless way required careful attention.
Accomplishments that we're proud of
Despite the time constraints, we successfully built a functional prototype of ClassLens. We managed to analyze classroom images and visualize the results in a way that would be useful to teachers. The user-friendly interface we designed ensures ease of access and provides valuable feedback for educators.
What we learned
Through this project, we learned how to practically integrate AI into real-world applications. It also taught us the importance of effective team collaboration, especially under tight time constraints. Additionally, we gained insights into improving model accuracy and dealing with the challenges of working with varied datasets.
What's next for ClassLens
Due to the limited timeframe of the hackathon, we have only demonstrated how ClassLens analyzes classroom images and displays the results. In the future, we plan to expand the system to include real-time video analysis for continuous monitoring of classroom attentiveness. Additionally, we will implement detailed student engagement metrics for deeper insights and partner with schools to test and refine the system with real users. Future plans also include adding features like multilingual support and personalization to cater to diverse educational needs.
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