Bet On UNDERstanding
Public Patent US20230401973A
- Engaging in e-learning content is hard
- Detecting user knowledge is even harder
- "Learn to earn" isn't working yet
- Content creators are poorly paid
- Crypto is scary or boring for non-experts
- Betting on quizzes
- Build a pretty & easy tool for massive adoption of non-crypto users
- Paying content creators for their work
However, the most important goal we can achieve is from the perspective of e-learning. Currently, there is no method for detecting accurately the user knowledge using multiple choices.
MCQ (multiple-choice question) is a powerful tool when you want to automatically "filter" for a certain degree of student knowledge (to be focused only on who passed a certain threshold). However, for a proper evaluation, an "essay question" is still needed, which is impossible to automate (yes, AI is not yet that powerful).
So, how can we more accurately evaluate still using an MCQ approach?
The solution comes from this white paper Evidential Multiple Choice Question (Ev-MCQ) from the researchers Javier Diaz, Maria Rifqi, Bernadette Bouchon-Meunier.
With Ev-MCQ the student answers in an imprecise way, setting confidence in the correctness of each answer and providing extra valuable information for its evaluation (to process automatically by an AI, yes in this case it can).
Here, the confidence of the user is challenged by the betting process for each answer (if students don't want to lose they have to seriously study).
BOUNDER is not only fun and economically sustainable for the stakeholders involved, but it is also extremely relevant for teaching purposes.
BOUNDER is a fundamental module of a bigger decentralised e-Learning platform we are building through our dedicated e-Learning DAO
