UNSW StudySpark

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Inspiration

Studying alone can be isolating, and finding the right study partner is challenging 😰. We wanted to create a platform that makes it easy for UNSW students to connect with study buddies based on their degree, subject, availability, and preferred study mode 👉👈. Inspired by the simplicity of Various Dating apps' swipe-based systems, we designed StudySpark to help students find the perfect match for productive study sessions.

What it does

StudySpark is a mobile app that helps UNSW students find study partners based on shared courses, availability, and study preferences. Key features include:

Swipe-based matching (Dating apps style) for study buddies
Profile customization (degree, subject, WAM range, study preferences, languages, MBTI, and more)
In-person and online study session options
Real-time chat system for coordination
Calendar sync for scheduling study sessions
Location-based discovery to find nearby students on campus
Profile pausing for when students don’t want to be matched

How we built it

Frontend: React Native (Expo), Material UI
Backend: Firebase Authentication, Firestore, Storage
Location Services: Google Maps API for proximity detection
Chat System: Firebase Firestore for real-time messaging
Scheduling: Google Calendar API integration

Challenges we ran into

Handling live chat, status updates, and location-based features efficiently in Firebase.
Prioritizing MVP features while maintaining a polished user experience.

Accomplishments that we're proud of

Developed a fully functional swipe-based matching system for seamless user connections. Integrated a dynamic real-time chat feature for efficient communication between users. Designed an intuitive, multi-step onboarding experience to guide users smoothly through the app. Created a "Liked" page where users can view those who have liked them after swiping right. Implemented real-time user information editing, allowing users to make instant profile updates.

What we learned

Efficient UI/UX design: Keeping interactions simple and intuitive enhances engagement. Firebase optimization: Best practices for structuring Firestore data for scalability and performance. Importance of user feedback: Early testing helped us refine study matching logic and feature prioritization. Balancing simplicity and functionality: Prioritizing core features over excessive complexity ensured a smoother development process.