Please describe your proposed solution
Bounder (meaning Bet on Understanding) is an educational "Learn to Earn" game engine. It can be considered an evolution of the famous quiz tool Kahoot.
Bounder is based on my public patent US20230401973A
where:
- multiple authors create knowledge assessments and receive tokens (for a job well done)
- students are assessed and receive tokens (for a good grade)
- when goals are not achieved, both parties lose tokens
The engine can be played in a “solo mode” (like a typical e-learning process) or “multiplayer mode” (like in a classroom, where the teacher interacts with multiple students in real time).
Currently, there is no method for detecting accurately user knowledge using multiple-choice questions.
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, an “essay question” is still needed for a proper evaluation, which is extremely hard to automate (yes, AI is not yet that powerful) properly.
So, how can we more accurately evaluate still using an MCQ approach?
One solution comes from the white paper “Evidential Multiple Choice Question” (Ev-MCQ) by Javier Diaz, Maria Rifqi, Bernadette Bouchon-Meunier.
With Ev-MCQ, student answers are intentionally imprecise because it is necessary to set the percentage of confidence in correctness, rather than focusing solely on identifying the right answer.
Bounder evolves this concept, introducing a betting mechanism to gamify the process and incentivise the learning process.
This system encourages learning while providing financial incentives for deserving students. It’s a win-win situation where both students and creators benefit!
Use case explanation
1. Quiz Introduction:
- Alice is a student who has a certain amount of tokens as a budget to spend.
- The budget can come from different sources such as patrons, sponsors, employers, parents and financially independent students.
- Bruno and Carl are teachers and quiz creators. They work collaboratively on each quiz to ensure students are well-evaluated. They receive tokens as incentivization for the job.
2. Quiz Creation and Token Incentives:
- Bruno and Carl create quizzes for students.
- For each question, Alice is asked to bet tokens on one or more answers per quiz.
- Every quiz has a value expressed in tokens.
- If Alice answers correctly, she wins tokens the token she bet; otherwise, she loses them.
- She cannot win more tokens than her initial budget.
3.“Learn to Earn” process:
- This system promotes learning and receiving payment for deserving students.
- It is particularly valuable in developing countries where salaries are low and students need support.
- If Alice does not know the answer, she may skip the assessment but pay a small fee as a penalty. Bruno and Carl will receive this amount.
- She can ask for hints in exchange for extra tokens. Again Bruno and Carl will receive it.
- Lost tokens due to wrong answers go to Bruno and Carl.
4. Budget Management:
- All tokens are deducted from the available budget.
- When the budget is consumed, Alice may top-up to continue with the examination/game.
- Bounder allows students to save the budget from wrong bets and cash out at the end of the evaluation.
5. Quiz Difficulty Balance:
- Bruno and Carl aim to provide non-trivial quizzes that challenge students.
- However, if quizzes are too difficult, Alice will always skip assessments and Bruno and Carl will not receive incentives.
- If the quizzes are too easy Alice will always win and Bruno and Carl will not receive incentives.
For a visual understanding of how it works, here is a simple wireframe: