CTEC Snap is a web application designed to streamline access to course and professor ratings from Northwestern University's internal evaluation system, CTEC. The platform allows students to easily search for courses, view detailed summaries of student reviews, and make informed decisions when selecting classes.
Accessing and interpreting course evaluation data at Northwestern University was a cumbersome and time-consuming process. Students had to navigate through a complex internal system (CTEC) to find relevant information about courses and professors. This made it difficult for students to make well-informed decisions when selecting classes, as they struggled to quickly access and understand the vast amount of review data available.
Initial Approach: To address the challenge of inefficient course evaluation access, we developed CTEC Snap. Our solution simplifies the process by:
1. Centralized Data Access: We scraped data from the internal CTEC system, creating a centralized database of course ratings and student reviews.
2. AI-Powered Summarization: Leveraging OpenAI's GPT model, we implemented a backend pipeline that generates concise summaries of student feedback for each course.
3. Intuitive Tagging System: We developed a system to automatically tag courses with pros and cons based on review content.
4. User-Friendly Interface: We designed a simple and intuitive frontend using React and Tailwind CSS for easy course searching and feedback viewing.
5. Real-Time Performance: By utilizing Firebase, we ensured real-time search capabilities and fast display of results, creating a seamless user experience.
Our solution not only simplifies the process of viewing CTECs but also provides valuable insights through AI-generated summaries and tags. This allows Northwestern students to make more informed decisions about their course selections efficiently.
Current Progress and Future Directions: We successfully developed this project during an 8-hour hackathon, winning 2nd place. Future improvements include enhancing search functionality, introducing user accounts for personalized recommendations, and adding more detailed metrics for review data visualization.