Easy Enroll

Inspiration

In the bustling college campus, the College Course Enrollment System was inspired by the need to simplify the course enrollment process for students and to make it fair and efficient. The vision was to build a system that would empower students to have a more significant say in their course preferences and also ensure that courses were assigned based on merit and relevance. This inspiration drove the development team to create a comprehensive web application with advanced matching algorithms and AI-driven tie-breakers.

What it does

The College Course Enrollment System is a web application developed in React.js and Spring Boot. It allows students to set their course preferences, prioritizes courses based on various parameters, and matches students with courses using an enhanced Gale-Shapley algorithm. The system also employs OpenAI's LLM API to resolve ties by assessing the relevance of student project input. Once the matching is done, the results are communicated back to the React.js UI.

How we built it

The project involved the collaborative efforts of a dedicated development team. The React.js web interface was designed to enable students to log in, view available courses, and prioritize them. The Spring Boot backend served as the API, implementing an enhanced Gale-Shapley algorithm for course-student matching. The algorithm considered student preferences, course capacities, prerequisite courses, and professor references. In cases of ties, OpenAI's LLM API was integrated to assess project relevance.

Challenges we ran into

The development team faced several challenges during the project's execution. Hosting the React.js frontend and Spring Boot backend on AWS was a significant task, requiring careful configuration and scaling considerations. Optimizing the algorithm was crucial to handle larger groups efficiently, and the team introduced a priority queue data structure to improve performance. Handling edge cases with unique student preferences required special algorithmic handling.

Accomplishments that we're proud of

The team successfully deployed the project on AWS, achieving a scalable and secure environment. The algorithm optimization, along with the use of a priority queue, significantly enhanced the matching process's speed and efficiency. The system was built to handle a wide range of student preferences and course offerings, making it robust and adaptable

What we learned

Through this project, the development team gained valuable experience in cloud deployment on AWS, algorithm optimization, and handling complex edge cases. They also harnessed the power of AI, integrating OpenAI's LLM API to resolve ties effectively.

What's next for Easy Enroll

The future of Easy Enroll holds exciting possibilities. The project can be expanded to include more intelligent decision-making processes, enhancing course recommendations and personalization. Additionally, user feedback and continuous improvement will be essential to make the system even more student-centric. Easy Enroll is poised to continue making the course enrollment experience more efficient and enjoyable for students and faculty alike.