Information and Visualization Imagery

Chapter Objectives

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  Define information and visualization imagery and their uses in e-learning

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  Explore the types of visualizations used in e-learning (in terms of dimensions, delivery devices, and extreme visualizations)

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  Investigate the processing spectrum of digital imagery for information and visualization

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  Describe some core components of complex, multi-stream visualizations

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  Explain the main reasons to visualize information

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  Contrast static with dynamic information visualizations

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  Study the value-added extraction of information from visualizations

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  Consider live data streams and complex multi-stream visual information

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  Address strategies for creating effective information imagery and visualization imagery

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  Describe how to design a full mental model

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  Explore how to avoid misconceptions or negative learning

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  Provide a method for evaluating information and visualization imagery

Introduction

Epidemiologists use these to track the spread of a disease through a population and to track how it is spread. Chemists view these to predict complex molecular interactions. Scientists use these (in part) to decide whether a spaceship should be launched into orbit. Architects use these to discuss a building’s floor plan. Economists examine various scenarios depicted by these. Environmentalists view these to plan how to go about protecting a forest habitat. Meteorologists use these to predict the general landfall of a hurricane. Archaeologists go to a website to check out the stratigraphy of an archaeological dig half a world away. Deep sea divers study these to get a sense of the particular environments that they’ll be entering. What these professionals refer to are information and visualization imagery.

Visualizations are a powerful affordance of digital and Web technologies. Foremost, these enable the communication of complex and information-rich details via imagery. They enable fresh ways of modeling information, particularly those with spatio-temporal aspects or large quantitative datasets. Designing visualization languages is “grounded in human conceptualization” (Douglas, Hundhausen & McKeown, 1995, p. 342). This field draws on typography, graphics, psychology and computer science. In recent decades, these have been designed to operate in computer and virtual environments.

These depictions, if designed around learning theory and instructional design, may increase the “learnability” of some course contents (Duchastel, 2003, pp. 310 – 311).