Set Theory, Calculus, Linear Algebra, Modern Algebra, Topology, Graph and Network Theory
Coding, Data-structures and Algorithms, Object Oriented Programming, Functional Programming, Molecular Programming
Optimization, Simulation, Machine Learning, Quantum Computing, DNA Computing, Cellular Automaton
We build your persionalized AI-guide to lead, evaluate and enhance your overall accomplishments.
School or college text-books are organized in tree structure of concepts. Our Learning materials are organized in a giant graph and tree data structure where connections are made through hierarchical relationship of contents, logics, basic concepts and learners average learning patterns. Each learner's learning trajectory is recorded, analyzed and recommendation is made while the achievement is coded into an array of scores. Let the learning be redefined!
An initial assessment is conducted on every learner's understanding of the subject matter. The initial state is declared through an initial assessment. As a learner starts to learn the concepts, he/she creates a personalized learning trajectory over the vast knowledge-space. As the learner propagates into the vast network of concepts, the learner's knowledge status is updated.
Every learner is a random walker visiting several nodes in the vast knowledge-space. In each node, a learner learns a specific concept. After completing the content, each learner is asked a specific quiz or problem to measure his/her accomplishment. When a learner visits 50-100 nodes, we prepare a learner's learning trajectory. After accomplishing a sufficient amount of nodes in a specific topic, a learner prepares a finger-printing of own learning attitude, understanding concepts as a personalized score (tensor).
When a learner learns a sufficient amount of concepts by visiting many concept nodes, we can simultaneously keep updating the learner's progress score in an array highlighting progress in basic math, logic, coding, theory etc. Progress report not only measures the learner's ability to solve the problem, it will also measure the efficiency, exploratory attitude, problem solving strategy development skills etc. Progress report is not created at the end, it is created side by side as a learner propagates into the knowledge-space.
As learners start to build up learning history, based on their learning ability, our AI based system activates the different level of exploration (e.g., accessing knowledge-space in research and articles in arxiv organized in graph data structure). On completing one concept, another higher concept in the hierarchy is recommended if required fundamental concepts are done. Recommendation can lead to research level as well. For example a curious explorer can learn and explore the concepts from recently added arxiv contents.
When a learner feels lacking in basic concepts (e.g., math, coding) before heading to learn the provided concept (e.g., product of matrices not understood while taking machine learning, a detour is recommended to an algebra brushup). Detour is recommended automatically when a learner is failed to answer or respond interactively in the learning platform. Learners can switch to basic concepts directly through links provided and come back based on their requirement.
A true structure of a concept or knowledge is a graph and network (e.g., knowledge-graph), not a text-book or a couple of pdf files. Cutting edge computational tools can enhance our learning and exploring exercise. Since our platform is personalized for a learner, it can also guide a researcher to explore vast amounts of knowledge space e.g., arxiv papers in Quantum Computing.