DSA By NOVA

login page
Dashboard/Profile
Practice:practice questions
History:see your history
Interview mode: for specific interview preparation.
Analytics: analyse your growth.

Inspiration

While learning DSA, there are too many problems and no clear path
Platforms give the same questions to everyone
Weak areas are not tracked properly
Learning feels random and confusing
Wanted a system that acts like a personal tutor

What it does

Tracks user performance and mistakes
Adjusts difficulty based on skill level
Gives hints instead of direct answers
Identifies weak topics and focuses on them
Includes Adaptive Interview Mode to simulate real interviews

How we built it

Frontend using React (Next.js)
Backend using Python (Flask)
PostgreSQL for storing user data
Decision engine (rule-based) to control learning flow
AI (LLM APIs) for hints and explanations
Deployed using Vercel and Render

Challenges we ran into

Designing the decision engine logic
Tracking user skills and weaknesses properly
Handling incorrect or vague AI responses
Connecting frontend, backend, and database smoothly
Managing real-time updates and performance

Accomplishments that we're proud of

Built a fully working adaptive learning system
Successfully integrated AI with decision logic
Created a realistic interview simulation mode
Deployed a complete full-stack application
Made learning more personalized and structured

What we learned

Importance of system design over just using AI
How to build and manage full-stack applications
Working with databases and cloud deployment
Writing better prompts for AI responses
Designing user-focused learning systems

What's next for DSA By NOVA

Add code execution and test cases
Improve mistake detection using ML
Enhance decision engine for better adaptation
Build a mobile-friendly version
Add more detailed analytics and insights