Knowledge Representation by Analogy for the Design of Learning and Assessment Strategies
Author(s) -
Walid Mestadi,
Khalid Nafil,
Raja Touahni,
Rochdi Messoussi
Publication year - 2017
Publication title -
international journal of modern education and computer science
Language(s) - English
Resource type - Journals
eISSN - 2075-017X
pISSN - 2075-0161
DOI - 10.5815/ijmecs.2017.06.02
Subject(s) - analogy , computer science , representation (politics) , artificial intelligence , knowledge representation and reasoning , human–computer interaction , knowledge management , epistemology , philosophy , politics , political science , law
The difficulty of learning a novel knowledge may not be the same for each learner. Often instructors try to find the easiest way to make a new knowledge understandable by most learners, but few of them give importance to the use of analogies which is the common practice in real life situations. This paper attempts to highlight that learning by analogy allows making the understanding of a novel and complicated knowledge easier. We first propose a model that represents the desired knowledge in an abstract way which allows us to find analogies in different domains. Based on these analogies, learning and assessment strategies can be derived in order to improve the learning outcome. Secondly, the design of a computer system that integrates the analogies, learning and assessment strategies into a digital learning environment is proposed.
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