The internet is full of information and is easily accessible to many of us here in Singapore. This gives us an ability beyond any other generation, to learn and gather knowledge on our own. However, we all have run into the problem where learning one thing requires us to learn another, which in turn requires us a learn another, leading into a dependency chain that descends into madness. This is why schools and universities, structure their courses to teach students in a progressive manner.

Ln (pronounced learn) solves that problem, by allowing you to learn any new concepts, anywhere. by yourself! Ln allows you to enter a keyword for whatever you want to learn, and forms a ln graph (knowledge dependency graph) which enables you to visualize what concepts are pre-requisites to your learning experience.

Ln doesn't stop there. Using web crawlers and summarizers (and a decent bit of artificial intelligence), Ln curates coursework and reading materials about any topic providing you with the resources to learn without ever leaving the app. It presents this data in a friendly card view which is both intuitive to use and beautiful to look at.

Ln is focused on simplicity, and right now is built with ES8 and Node along with backend algorithms written in python. It also uses a graph database to store and access the ln graph. We also use javascript visualization tools, like d3.js and anime.js to make the user interface interactive and interesting.

In its current state, Ln is simply a Minimum Viable Product (MVP). However, features such as automated MCQ question generation and user profiling can bring Ln to a whole new level. MCQ question generation boosts knowledge retention of the users allows them to gauge their progress. Meanwhile, user profiling keeps track of a user's previous learnt topics and can provide personalized study aids and user specific ln graphs. A few other future features are listed below.

  • User Personalization
  • Generated Questions
  • More sources of scraped data (Youtube, Research papers)
  • Machine Learning to identify more knowledge features in data
  • and probably a lot more
  • Deeper dependency nesting and graph formatting

Team ID : BL

Team Members

  • Ambrose Chua: 684
  • Isaac Tay: 763
  • Sudharshan: 656

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