Visualizing the Invisible: Generating Explanations of Scientific Phenomena
Many topics in science are notoriously difficult for students to learn. Mechanisms and processes that exist on a scale outside student experience present particular challenges. While instruction often involves visualizations, students typically explain in words, spoken or written. Visualizations have many advantages over verbal explanations, especially for science, so asking students to produce visual rather than verbal explanations should improve their learning. This hypothesis was tested in two domains, a mechanical system and a chemical system. The explanations were analyzed for content, and learning assessed by a post-test. Participants' spatial ability was also assessed as spatial ability often correlates with learning science. For the verbal explaining of a mechanical system, the bicycle tire pump, high spatial participants performed better than low spatial participants. However, low spatial participants performed better and as well as high spatial participants after producing visual explanations. Visual explanations included significantly greater amounts of structural information, as well as other features essential to understanding function, for example a complete explanation of the inlet valve, a crucial but invisible structural component. In the domain of chemical bonding, visual explanations were more effective than verbal explanations, and high spatial ability participants showed greater learning than low spatial ability participants. Visual explanations contained a significantly greater amount of structural information, made reference to specific examples of chemical compounds, and often contained supplementary text. Text added to visual explanations predicted post-test scores, as did the inclusion of invisible features. Many participants who drew identified actual examples of ionic and covalent molecules. Written explanations often used general terms and presentations of definitions. Explanations generated by high spatial ability participants contained greater amounts of function and were more likely to include specific examples. In both domains, text was often spontaneously added to visual explanations. In Experiment 1, added text was equally likely to describe structure or function; in Experiment 2, added text was more likely to describe function. Taken together, the studies provide support for the use of learner-generated visual explanations as a powerful learning tool and suggest that visual explanations are superior because they demand and provide a check for completeness of explanations.
Networked Digital Library of Theses and Dissertations