A modern, literate person is one who is not only able to read and write but is educated in all the basic means necessary to thrive in a digital, networked world.
An important aspect of this general literacy is a digital visual literacy, the ability to critically analyze visual materials, create effective visual communications, and make judgments and decisions using visual representations of thoughts and ideas.
Digital visual literacy is a set of skills that enable students to function in an increasingly digital and visual workplace. These skills are based on concepts from a range of established disciplines but are not simply a collection of modules from courses in, say, computer science and graphic design; they build on basic concepts in such disciplines but are modified with awareness of related skills in other disciplines. The basic DVL skills are informed by original sources in single disciplines and interdisciplinary projects. Ideally, students should learn DVL skills in authentic contexts, such as learning how to make a business presentation, rather than study them solely in the abstract.
One of the challenges of defining a field of DVL and describing its contents is that research on the visual has been carried on in many fields, but without, until recently, much cross-disciplinary influence. Imagine, if you would, that mathematics was taught and conceptualized very differently in different fields: “Engineering math” might have different notations, axioms, methods of proving things and goals than “aesthetic math” and both might seem foreign to, say, a group of number theorists. The groups wouldn’t be able to leverage each others’ knowledge because it would be so hard to translate between them. A student seeking to be mathematically literate would have to undertake studies in several academic departments to get any sense of a larger mathematical picture. There wouldn’t be any “basic math” to teach in an introductory course because the “basics” would be different depending on the field the student was going into.
The field of DVL is like this today. Although one needs access to concepts that originate in several different fields, there are few resources for non-experts that relate a field’s concepts to DVL. Terminology in media theory and the computer science of computer graphics, for example, fall into two circles that scarcely overlap at all. In addition to terms incomprehensible to one group or the other, often the same words mean different things in different fields. The key phrase “computer graphics,” for instance, means something quite different to a computer scientist than to a graphic designer.
Art and design, and often visual literacy courses, tend toward a studio-based approach that includes drawing and compositional skills and an extensive vocabulary to describe images (line, form, color, etc.). Media and culture theory tend toward highly politicized views of image meaning and purpose and often draw on ideas in philosophy, economics, and linguistics. Vision science, an increasingly vital part of the “brain sciences,” has progressed dramatically in the past 50 years, in part through trying to teach computers to “see.” This research is directly applicable to creating and interpreting images, especially those made on the computer, but, once again, the underlying concepts and terminology are completely different from the fields mentioned before. A novice cannot casually read a research paper in vision science.
The NSF Grant
As international culture and commerce become increasingly reliant on visual communications, visual literacy is becoming an essential skill for technical workers and college graduates. Supported by a three-year, $600,000 grant from the National Science Foundation to the Maricopa Community Colleges, this project set out to develop pioneering curriculum modules for teaching digital visual literacy.
Brown University provided input from its research activities in the domain of visualization and digital imagery. Participation by Brown University ended early in 2007. The Maricopa Community College District completed the deployment, evaluation and dissemination activities.
This material is based upon work supported by the National Science Foundation Grant No. 0501965.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).