A Core of Granular Knowledge Graph and Adaptive Personalization

funtoot has deconstructed every concept into granular units of learning. For example, the world of math is classified as per common standards into learning concepts and sub-concepts. At the lowest level are atomic learning units which address a set of learning gaps.

This granular decomposition enables funtoot to monitor learning gaps as a child explores each concept.

funtoot problems are created to allow identification of specific learning gaps in a concept and enable self-correction through different types of assistance.

By encouraging learning through self-correction, funtoot helps remove and resolve permanently, any learning gaps in the concept

funtoot problems are mapped across varied complexity levels from comprehension to application. Every child is then taken through a unique set of problems, starting with lower levels of complexity, and ending with higher order complexity.

Thereby encouraging them to understand concepts, eliminate learning gaps and apply the learning to real world problems.

AI-based learning curve formulation for every child resulting in personalised 1:1 learning.

Automatic correction and evaluation of descriptive answers.

Predictive algorithms to forecast the student’s performance in the next question or concept.

Student logs and data fed back to funtoot to enhance adaptive personalization and update the core models.

Unlimited questions customized to every child, to ensure mastery learning.

Recommendation of next question, sub-topic and topic based on child’s unique performance.