vcandelario

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We learned that students have issues finding analytics towards professors for the university and being able to speak with students on any recommendations and suggestions. We decided to create a website similar to the application known as WildFire where students can chat with each other and discuss plans toward creating course schedules all through a singular source. Students are able to access a student base chatroom that requires login using their google mail. After logging in, students have access to a chatroom with other students. We split our team into two groups which were the front end and back programmers. Front-end programmers used HTML and CSS using create-react-app to create the basis of the login and chatroom usage. The back-end programmers used Python through a third-party script editor popularly known as Visual Studio Code. We created a real-time database using Firebase where we used python to scrap information from RateMyProfessor.com to retrieve useful analytics using metadata from each professor such as their name, difficulty, and overall rating. All the information was sent to the database and pushed into a repository located in Github. The reason we used Python is that the code written and information retrieved was compatible with the HTLM based website and could be transferred over. We had many issues regarding pulling the analytical information and being able to store it in the database. After the issue was resolved, it was pushed to the Github repository. There wasn't much time to do more with the website in order to allow students to be able to create a custom course schedule. We were able to allocate the analytics information to our database for later usage. Our front-end developers were able to get the chat system working, even where information from the chat system can be stored if there are any means to look back at its history which was a great accomplishment for the development team. Our team overcame issues in regards to learning how to implement our database into GitHub and discover many ways to scrap data using different packages allocated in Python. We want to optimize vcandelario to its full potential. It not only works on a laptop/desktop but even mobile as well. At this point, we only have analytics from the University of California Merced, but something to work towards is having it work for the entire UC community.