Understanding Graph and Understanding Map and their Potential Applications
This work proposes incremental conceptual tools for knowledge representation and learning optimization in domains like education or AI.
This paper introduces Understanding Graph and Understanding Map as extensions of the Understanding Tree concept, exploring their potential applications such as measuring concept complexity and importance, and computing optimized learning sequences.
Based on the previously proposed concept Understanding Tree, this paper introduces two concepts: Understanding Graph and Understanding Map, and explores their potential applications. Understanding Graph and Understanding Map can be deemed as special cases of mind map, semantic network, or concept map. The two main differences are: Firstly, the data sources for constructing Understanding Map and Understanding Graph are distinctive and simple. Secondly, the relations between concepts in Understanding Graph and Understanding Map are monotonous. Based on their characteristics, applications of them include quantitatively measuring a concept's complexity degree, quantitatively measuring a concept's importance degree in a domain, and computing an optimized learning sequence for comprehending a concept etc. Further study involves evaluating their performances in these applications.