Inspiration
Every registration season, UMD students face the same anxiety: "Can I actually make it from IRB to Art-Soc in 10 minutes?" We wanted to transform schedule planning from stressful guesswork into data-driven decision-making, so students know whether their schedule is physically feasible before classes even start.
What it does
CoursePal analyzes your intended class schedule and calculates two critical metrics: the lateness risk for each transition (based on walking distances between buildings) and the enrollment probability (using historical seat availability data). Students input their schedule, click "Calculate Risk," and instantly see which class transitions are dangerous, which sections are likely to fill up, and what alternative sections could reduce their overall risk.
How we built it
We built CoursePal using TypeScript and React for a clean, responsive UI/UX. We leveraged Replit Core for rapid development and deployment, and worked extensively with Claude AI to ideate features, design algorithms, and solve technical challenges. The core engine uses the Haversine formula to calculate walking distances between campus buildings, a sigmoid function to score lateness risk (0-100), and historical Testudo seat data to predict enrollment probability.
Challenges we ran into
Our biggest challenge was time constraints, we had ambitious plans but limited hours. We intended to integrate the PlanetTerp API to pull grade distributions and professor reviews, then use sentiment analysis to "roast" your schedule by highlighting difficult classes and tough professors. Unfortunately, we had to cut this feature to focus on core functionality. We also struggled with accurately modeling walking times, since campus isn't a flat grid, students navigate construction zones, stairs, and crowds that simple distance calculations miss.
Accomplishments that we're proud of
We're proud of building a fully functional, production-ready tool that solves a real problem every UMD student faces. We successfully implemented accurate walking time calculations for 20+ campus buildings, created an intuitive risk scoring system that matches student intuition, and designed a beautiful interface that presents complex data clearly. Most importantly, we validated our concept with real student schedules, CoursePal correctly flagged every "impossible" transition we tested.
What we learned
We learned the importance of ruthless prioritization in hackathons—great ideas have to be cut to ship something complete. We deepened our understanding of geospatial calculations (Haversine formula) and mathematical modeling (sigmoid risk curves). Working with Claude AI taught us how powerful AI-assisted development can be for rapid prototyping and problem-solving. We also learned that real-world data is messy—Testudo seat numbers fluctuate, building entrances change, and students walk at different speeds.
What's next for CoursePal
Next, we plan to implement the PlanetTerp integration for grade and professor analysis, add live Testudo API scraping for real-time seat availability, and introduce crowdsourced walking time data where students report actual transition times. We want to build a mobile app for on-the-go schedule adjustments, add accessibility features for ADA-compliant routes, and expand beyond UMD to other universities. Our ultimate vision: make CoursePal the go-to tool for college schedule planning nationwide.
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