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ClassSnap:

Problem Statement:

Traditional student information management systems often struggle to keep up with the increasing demand for streamlined administrative processes and personalized learning experiences. In parallel, online learning platforms have become prevalent, offering extensive course materials and digital notes. However, students often face challenges in efficiently processing and assimilating this much of information, leading to reduced learning effectiveness and academic performance.

Proposed Solution:

ClassSnap is an advanced web application designed to enhance student’s learning experience by providing personalized access to academic details and automatically generating comprehensive notes through intelligent presentation summarization. Leveraging cutting-edge technologies, the platform employs Machine Learning algorithms to perform intelligent summarization of presentations from online class sessions. Through the use of Selenium Web Driver, ClassSnap automatically extracts relevant information from the meeting presentations and processes it with the Machine Learning algorithm to generate organized and concise notes, reducing manual effort for students. The application seamlessly integrates with popular online learning platforms, ensuring effortless synchronization of academic data.

Demo Video

Watch our demo video to see ClassSnap in action.

Contributing

  • Please check the Contributions file for instructions on how to get started with the project on your local system
  • Contributions are welcome! Feel free to fork the repository, make improvements, and create pull requests.
  • Please view the list of open issues at Issues. Any contributions to them are welcome.

Tech Stack

  • MERN Stack:
    • MongoDB
    • Express.js
    • React.js
    • Node.js
  • Python's Selenium: Used for web scraping.
  • Python OCR (Optical Character Recognition) Engine: Processes visual content within presentations.

Authors

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License.

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  • JavaScript 65.9%
  • Python 32.2%
  • CSS 1.1%
  • HTML 0.8%