The Science of Visual Data Communication: What Works

Effectively designed data visualizations allow viewers to use their powerful visual systems to understand patterns in data across science, education, health, and public policy. But ineffectively designed visualizations can cause confusion, misunderstanding, or even distrust—especially among viewers with low graphical literacy. We review research-backed guidelines for creating effective and intuitive visualizations oriented toward communicating data to students, coworkers, and the general public. We describe how the visual system can quickly extract broad statistics from a display, whereas poorly designed displays can lead to misperceptions and illusions. Extracting global statistics is fast, but comparing between subsets of values is slow. Effective graphics avoid taxing working memory, guide attention, and respect familiar conventions. Data visualizations can play a critical role in teaching and communication, provided that designers tailor those visualizations to their audience.

Computer-Based Experiment of Free Fall Movement to Improve the Graphical Literacy

<p>The purpose of this study is to develop computer-based experiment of free fall movement to improve the graphical literacy of the Physics Education students. This study employed quasi-experimental design, particularly the pre-test and post-test control group design. The sample was 58 students of the first semester of Physics Education study program. They were divided into two groups, namely 38 students constituted an experimental group and 20 students constituted the control group. Both groups joined learning activities at laboratory and classrooms with the same topic which is the free fall movement. Computer-based learning models were used to design learning activities for the experimental group, while the control group used a traditional learning model with manual experiment with stopwatch and never use experiment tool of free fall movement. It was indicated in this study that the use of such learning model improved the graphical literacy capability better than the traditional learning, which covered ability to identify experimental variables, using the experimental tools, making a graph, formulating mathematical equation, making predictions on the basis of the graph.</p>