Developing Novel Explanatory Models for Electronics Education.

This paper explores how representations of technologicalconcepts may be designed to help students with visuallearning styles achieve successful comprehension in thefield of electronics. The work accepts a wide definition ofwhat is understood by the visualisation of a model in thatit can take different external forms, but also include aninternal representation in a person’s mind. We are of theopinion that to acquire scientific or technologicalknowledge there is a requirement for abstract models toexhibit particular features that complement the nature oftheir fields, and that their effectiveness is dependent onthe context in question. This work reports on thedevelopment of experimental materials which are novelteaching aids in the context of electronics education. Itproposes design principles based on congruent,schematised, symmetrical spatial metaphors of circuitsincorporating interactivity by the use of gesture,scaffolding, learning by topological, analogical andconceptual resemblances. We conclude that qualitativemethods may be employed with a significant measure ofsuccess even for a field such as electronics that is oftenconsidered to be difficult due to the necessity of abstractexplanations.

[1]  Eddie Norman Models of Change: 'The impact of 'designerly thinking' on people's lives and the environment... Ken Baynes , 2009 .

[2]  Janice D. Gobert,et al.  Leveraging Technology and Cognitive Tehory on Visualization to Promote Students’ Science , 2005 .

[3]  Peter C.-H. Cheng,et al.  Unlocking conceptual learning in mathematics and science with effective representational systems , 1999, Comput. Educ..

[4]  Martha W. Alibali,et al.  Gesture in Spatial Cognition: Expressing, Communicating, and Thinking About Spatial Information , 2005, Spatial Cogn. Comput..

[5]  Jiajie Zhang,et al.  The Nature of External Representations in Problem Solving , 1997, Cogn. Sci..

[6]  Michael J. Spivey,et al.  Eye Movements and Problem Solving , 2003, Psychological science.

[7]  D E Egan,et al.  Chunking in recall of symbolic drawings , 1979, Memory & cognition.

[8]  Samuli Kolari,et al.  Visualisation Promotes Apprehension and Comprehension , 2004 .

[9]  Martha W. Alibali,et al.  I see it in my hands’ eye: Representational gestures reflect conceptual demands , 2007 .

[10]  Jiajie Zhang,et al.  Representations in Distributed Cognitive Tasks , 1994, Cogn. Sci..

[11]  Barbara Tversky,et al.  Prolegomenon to Scientific Visualizations , 2005 .

[12]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987, Cogn. Sci..

[13]  John K. Gilbert,et al.  Visualization: A Metacognitive Skill in Science and Science Education , 2005 .

[14]  Peter C.-H. Cheng,et al.  Electrifying diagrams for learning: principles for complex representational systems , 2002, Cogn. Sci..

[15]  David N. Rapp,et al.  Mental Models: Theoretical Issues for Visualizations in Science Education , 2005 .

[16]  Jon Oberlander,et al.  A Cognitive Theory of Graphical and Linguistic Reasoning: Logic and Implementation , 1995, Cogn. Sci..