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Differential-associative processing: A new strategy for learning highly-similar concepts

机译:差分关联处理:学习高度相似概念的新策略

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For many students concepts like fluid and crystallized intelligence are difficult to learn because they have highly-similar definitions that are easy to confuse. The challenge of learning these highly-similar, yet often confused concepts is further complicated by the fact that students are tested on exams about differences between the concepts. In this theoretically-motivated research we test a new strategy for learning highly-similar pairs of concepts, called differential-associative processing. The results revealed that performance on multiple-choice questions was higher when students learned highly-similar concepts using differential-associative processing rather than a strategy of their own choice, text-based elaboration, or identifying similarities and differences. The results also revealed that students spontaneously transferred differential-associative processing to a neutral control condition. Taken as a whole, the present study supports differential-associative processing as a useful strategy for learning similar concepts. Copyright © 2009 John Wiley & Sons, Ltd.
机译:对于许多学生来说,诸如流体智能和结晶智能之类的概念很难学习,因为它们具有非常相似的定义,容易混淆。由于要通过关于概念之间差异的考试对学生进行测试,因此学习这些高度相似但经常混淆的概念的挑战变得更加复杂。在这项基于理论的研究中,我们测试了一种用于学习高度相似的概念对的新策略,称为差分关联处理。结果表明,当学生使用差异联想处理而不是他们自己选择的策略,基于文本的阐述或识别异同来学习高度相似的概念时,多项选择题的表现会更高。结果还表明,学生自发地将微分联想处理转移到中性控制条件。总的来说,本研究支持差分联想处理作为学习相似概念的有用策略。版权所有©2009 John Wiley&Sons,Ltd.

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