Study Buddy

Inspiration

University life can be isolating, and finding study partners who share similar goals, courses, and learning styles can be challenging. We wanted to create a platform to foster collaboration and academic success.

What it does

Study Buddy matches students with ideal study partners based on shared academic goals, enrolled courses, learning styles, and availability. It also schedules study sessions, gamifies progress with leaderboards, and stores profiles and ratings for better matchmaking.

How we built it

We built the backend using a combination of JavaScript and Django, with Django handling API development and data management. The frontend uses plain HTML and CSS for simplicity and accessibility. For the database, we implemented SQLite to store profiles, schedules, and ratings. A custom-built matching algorithm, developed from scratch, leverages k-means clustering to group students based on shared goals, courses, and preferences, ensuring accurate and effective matches.

Challenges we ran into

Integrating the course catalog dynamically was tricky, as was designing an intuitive UI that accounted for diverse schedules. We also had to fine-tune the matching algorithm for accuracy and scalability.

Accomplishments that we're proud of

We’re proud of building a platform that promotes academic collaboration. The dynamic matchmaking system and gamified leaderboard stood out as significant achievements, offering unique value to users.

What we learned

We learned how to optimize AI clustering algorithms for real-time matching, enhance UI/UX design for usability, and address scalability challenges in database architecture and API integration.

What's next for Study Buddy

We plan to expand Study Buddy to support other universities, add group study sessions, integrate productivity tools, and refine the matching algorithm using feedback and advanced analytics.