DeFT: A conceptual framework for considering learning with multiple representations
DeFT: A conceptual framework for considering learning with multiple representations
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DOI：
10.1016/j.learninstruc.2006.03.001
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发表时间：
2006-06-01
影响因子：
6.2
通讯作者：
Ainsworth, Shaaron
中科院分区：
文献类型：
--
作者：
Ainsworth, Shaaron
当人们学习复杂的新思想时，多重（外部）表征可以提供独特的好处。不幸的是，许多研究表明，这一承诺并不总是实现。多元表征学习的DeFT （Design, Functions, Tasks）框架整合了学习研究、表征认知科学和建构主义教育理论。它提出，通过考虑学习的三个基本方面，可以最好地理解多表征的有效性：多表征学习特有的设计参数；多重表征在支持学习中的功能，以及学习者与多重表征交互时必须承担的认知任务。该框架的用途是确定影响学习的广泛因素，协调不一致的实验结果，揭示多表征研究中未被探索的领域，并指出使用多表征学习的潜在设计启发式。(c) 2006 Elsevier Ltd.版权所有。
Multiple (external) representations can provide unique benefits when people are learning complex new ideas. Unfortunately, many studies have shown this promise is not always achieved. The DeFT (Design, Functions, Tasks) framework for learning with multiple representations integrates research on learning, the cognitive science of representation and constructivist theories of education. It proposes that the effectiveness of multiple representations can best be understood by considering three fundamental aspects of learning: the design parameters that are unique to learning with multiple representations; the functions that multiple representations serve in supporting learning and the cognitive tasks that must be undertaken by a learner interacting with multiple representations. The utility of this framework is proposed to be in identifying a broad range of factors that influence learning, reconciling inconsistent experimental findings, revealing under-explored areas of multi-representational research and pointing forward to potential design heuristics for learning with multiple representations. (c) 2006 Elsevier Ltd. All rights reserved.