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The Benefits and Challenges of Analogical Comparison in Learning and Transfer
Author(s) -
Shiva Hajian
Publication year - 2018
Publication title -
sfu educational review
Language(s) - English
Resource type - Journals
ISSN - 1916-050X
DOI - 10.21810/sfuer.v11i1.599
Subject(s) - analogy , generalization , analogical reasoning , categorical variable , computer science , process (computing) , artificial intelligence , cognitive science , psychology , cognitive psychology , machine learning , epistemology , philosophy , operating system
There is ample evidence that analogy can be employed as a powerful strategy for learning new concepts, transferring knowledge, and promoting higher level thinking. Similarly, self-explanation has been shown as an effective strategy in learning, integrating new information with prior knowledge, and monitoring and revision of previous mental models (Chi et al., 1989). While both of these strategies are considered efficient scaffolding in the field of instruction and learning, each individual strategy has its own limitations and constraints such as overgeneralization, disregarding details, and possible erroneous reasoning. To investigate whether these constrains can be overcome, a review of literature was conducted and each individual scaffolding strategy was studied. At the end, the potential benefits of integrating both strategies – generating explanation using analogical comparison – were discussed. It was hypothesized that prompting learners to explain analogical cases (analogy induced self-explanation) may greatly enhance learning through activation of prior knowledge, structured linking, categorical learning and higher order thinking. This integration may lead to a revised model of self-explanation with higher productivity and less constraints on the process of knowledge acquisition and generalization.

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