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Probably Good Diagrams for Learning: Representational Epistemic Recodification of Probability Theory
Author(s) -
Cheng Peter C.H.
Publication year - 2011
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/j.1756-8765.2009.01065.x
Subject(s) - diagrammatic reasoning , computer science , comprehension , cognitive science , notation , representation (politics) , encoding (memory) , domain (mathematical analysis) , artificial intelligence , psychology , mathematics , mathematical analysis , arithmetic , politics , political science , law , programming language
The representational epistemic approach to the design of visual displays and notation systems advocates encoding the fundamental conceptual structure of a knowledge domain directly in the structure of a representational system. It is claimed that representations so designed will benefit from greater semantic transparency, which enhances comprehension and ease of learning, and plastic generativity, which makes the meaningful manipulation of the representation easier and less error prone. Epistemic principles for encoding fundamental conceptual structures directly in representational schemes are described. The diagrammatic recodification of probability theory is undertaken to demonstrate how the fundamental conceptual structure of a knowledge domain can be analyzed, how the identified conceptual structure may be encoded in a representational system, and the cognitive benefits that follow. An experiment shows the new probability space diagrams are superior to the conventional approach for learning this conceptually challenging topic.