Kluster

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EdTech

Intelligent Learning Connections

Summary

Build an AI-powered application that uses a collaborative learning matching system designed to enhance collaborative learning experiences by intelligently pairing learners based on their performance metrics.

Requirements

Implement a Machine learning model/matching engine based on student performances.
Build a user-friendly interface.
Establish a feedback loop where learners provide feedback on their collaborative experiences, which would be used to refine the model.
Data for building the model can be found here.

Submission Guidelines

Figma (Optional): Provide a link to your Figma design / prototype for your project.
GitHub Repository: Provide the link to your GitHub repository containing all the relevant source code and project files.
Deployed Link: Share the link to the deployed version of your project. For web applications, consider hosting on platforms like Netlify or Heroku. If it's a mobile app, upload the APK to Google Drive and provide a public link.
Walkthrough/Presentation: Create a concise 2 to 5-minute maximum screen recording. Walk through key features, functionalities, and any unique aspects of your project. Clearly articulate how users can interact with and benefit from your solution. Ensure your presentation covers essential details without exceeding the time limit.

Suggested Approach

Day 1 - 2: Conceptualization & Design

Product Manager & Product Designer:

Define user flows for implementing the machine learning model and creating a user-friendly interface.
Design an intuitive and user-friendly platform for learners to access and provide feedback.

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Day 3 - 4: Development & Testing

Data Science AI/ML:

Implement the machine learning model/matching engine based on student performances.
Integrate the model into the collaborative learning platform.

Frontend:

Develop an accessible and user-friendly interface for learners to access the collaborative learning platform.

Day 5 - 6: Refinement, User Feedback Integration, & Presentation

All Team Members:

Establish a feedback loop mechanism for learners to provide feedback on their collaborative experiences.
Integrate user feedback mechanisms for usability improvements.
Conduct extensive testing for reliability and ease of use.
Finalize the application, focusing on simplicity, accuracy, and user satisfaction.

Prepare a Presentation:

Showcase the application's capabilities, emphasizing its potential to enhance collaborative learning experiences through intelligent learner pairing based on performance metrics.

Build an AI-powered application that uses a collaborative learning matching system designed to enhance collaborative learning experiences by intelligently pairing learners based on their performance metrics.

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Day 1 - 2: Conceptualization & Design

Product Manager & Product Designer:

Day 3 - 4: Development & Testing

Data Science AI/ML:

Frontend:

Day 5 - 6: Refinement, User Feedback Integration, & Presentation

All Team Members:

Prepare a Presentation:

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