Cheng, Peter C-H (2002) Electrifying diagrams for learning: principles for effective representational systems. Cognitive Science, 26 (6). pp. 685-736. ISSN 0364-0213

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Abstract

Six characteristics of effective representational systems for conceptual learning in complex domains have been identified. Such representations should: (1) integrate levels of abstraction; (2) combine globally homogeneous with locally heterogeneous representation of concepts; (3) integrate alternative perspectives of the domain; (4) support malleable manipulation of expressions; (5) possess compact procedures; and (6) have uniform procedures. The characteristics were discovered by analysing and evaluating a novel diagrammatic representation that has been invented to support students' comprehension of electricity—AVOW diagrams (Amps, Volts, Ohms, Watts). A task analysis is presented that demonstrates that problem solving using a conventional algebraic approach demands more effort than AVOW diagrams. In an experiment comparing two groups of learners using the alternative approaches, the group using AVOW diagrams learned more than the group using equations and were better able to solve complex transfer problems and questions involving multiple constraints. Analysis of verbal protocols and work scratchings showed that the AVOW diagram group, in contrast to the equations group, acquired a coherently organised network of concepts, learnt effective problem solving procedures, and experienced more positive learning events. The six principles of effective representations were proposed on the basis of these findings. AVOW diagrams are Law Encoding Diagrams, a general class of representations that have been shown to support learning in other scientific domains.

Item Type:	Article
Keywords:	Representations; Diagrams; Conceptual learning; Problem solving; Electricity; Task analysis; Science education
Schools and Departments:	School of Engineering and Informatics > Informatics
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Depositing User:	Peter Cheng
Date Deposited:	03 Oct 2008
Last Modified:	30 Apr 2018 14:59
URI:	http://srodev.sussex.ac.uk/id/eprint/1935
Google Scholar:	51 Citations

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