Education Recommendation website

Website prompts and questions part 1
Website prompts and questions part 2
Website prompts and questions part 3
Large Text Feature
Text to speech option
Localisation feature

Selected Track: Access to Education

AI acknowledgement: We have used ChatGPT in the development with the app to get a framework for the code and we made small adjustments to fit our personal preferences. We used the streamlit library for python to host our website.

Github Repository link: https://github.com/nicholastran716-lang/Recommendation_edication_site_website-HACKMELBOURNE-.git

Inspiration

We were inspired by the United Nations Sustainable Development Goals, which highlight that many children and young people still lack the digital skills needed to use the Internet effectively and safely. We wanted to help students more easily find educational resources that suit their individual needs, preferences, and circumstances.

What it does

Our website allows students to enter preferences such as:

Subject
Learning style
Language
Region
Accessibility needs

Based on these preferences, the website recommends educational resources that are most relevant to the student.

How we built it

We built our project using Python and Streamlit, with Visual Studio Code as our main development environment.

We created a recommendation system that compares the user's selected preferences with information stored about different educational websites. The system then returns the resources that best match the user's needs.

We also used GitHub for version control and collaboration, allowing multiple team members to work on the project while keeping track of changes.

Challenges we ran into

One of our first challenges was deciding on a project idea that meaningfully addressed the Accessible Education track.

After choosing our idea, we had to decide which features would provide the most value to students and how we could implement them within the limited time available. We also faced challenges while integrating different features and coordinating changes between team members using GitHub.

Accomplishments that we're proud of

We are proud that we were able to create a working recommendation system that suggests educational resources based on a student's individual preferences.

We were also able to include factors such as language, region, learning preferences, and accessibility needs, helping make the recommendations more relevant to different types of learners.

What we learned

Throughout the project, we learned how to:

Build an interactive web application using Streamlit
Develop a recommendation system using Python
Use VS Code effectively for development
Use GitHub for version control and team collaboration
Manage tasks and work effectively as a small development team

Most importantly, we learned how to turn an initial problem into a functional prototype within the time constraints of a hackathon.

What's next for Education Recommendation Website

Our next goal is to significantly expand our database of educational resources.

Currently, the system mainly contains larger and more well-known educational websites. By adding more specialised and regional resources, we could provide more personalised recommendations for students with different subjects, languages, locations, accessibility requirements, and learning preferences.

In the future, we would also like to improve the recommendation system so that it can consider a wider range of factors and provide even more accurate results.