Vol.34 No.1 February 2000

Teaching Computer Graphics Visual Literacy to Art and Computer Science Students

Dena Eber
Bowling Green State University

Rosalee Wolfe
DePaul University

February 00 Columns
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Instructors in both art and computer science departments experience difficulties in motivating students to develop a visual literacy in computer graphics. Although a highly prized skill in industry, visual literacy is intimidating to computer science students because they are uneasy about using their eyes to examine computer-synthesized images. Even though they are used to using their eyes for acquiring information, the topics of analysis and interpretation intimidate art students. Further, art students may not have as much background to understand the technical terminology. This first column in a two-part series discusses an interdisciplinary approach for teaching visual literacy that overcomes these obstacles. With this approach students become more familiar with the limits and possibilities of the medium of computer graphics, learn how to analyze and talk about what visual images might mean and develop a deeper understanding of time constraints. In addition, they gain confidence with technological terminology and the idea of suggesting alternative algorithms to create a desired visual "look." As a result, both computer science and art students become more able to communicate effectively about and with visual imagery.


Visual literacy, a term often used by the art educator Vincent Lanier [4], is important in graphics for both computer science and art students and is highly prized in the computer graphics industry [7, 6, 5]. Once hired, graduates will be working in an interdisciplinary team-oriented environment where communication is an essential skill for successful collaboration. Visual literacy provides a shared vision and terminology for communication.

In their role as facilitator, computer science graduates will need visual literacy in order to suggest algorithms to create a desired "look." They need to know the cost involved. If it’s prohibitive, they should suggest alternatives that are visually similar. When necessary, they should be able to develop new techniques in response to a need that may include a description or depiction of a visual effect.

For art students, visual literacy in computer graphics imparts an understanding of the technical terminology and the associated visual cues that help them communicate their intentions to colleagues, gallery directors and the viewing public. Artists in both industry and in the art world of galleries, museums and academia are expected to talk about their work on both a formal and conceptual level. In industry, artists need to communicate to members of cross-disciplinary teams, and in the art world, to the general public or those familiar with some form of artistic language. In either case, a visual literacy gives students a standard and consistent means to communicate to different audiences. The technical terminology associated with this dialogue is essential to fully express their ideas.

By having students verbally describe their formal and conceptual ideas to varying audiences, they are forced into thinking about the formal qualities of their work and what it may mean. Often artists simply create a work of art without understanding how people may read it. A language of common visual terms will help them think about their work, communicate their visual intentions and most importantly, understand how others will interpret their visual imagery.

Visual literacy also includes understanding the cost of certain artistic choices. For example, a student might believe that ray tracing his or her animation will create a stronger story. In fact, ray tracing a five-minute animation is not only prohibitively expensive, but the time-based quality of the animation will mask any subtle differences the extra cost will produce and not change the formal or conceptual success of the story.

In a sense, cost is a limit of the digital medium. In addition to cost, students often approach the digital tool as if it were a paint or sculpture studio. The medium is unique and provides qualities which more traditional media cannot. Visual literacy helps the student understand how digital art is related to, but different from, traditional art tools.

Although visual literacy in computer graphics is highly prized in industry and in the art world, there is a marked reluctance for faculty to discuss it in the classroom. For computer science faculty, computer graphics is unique in that it has an essential visual element that must be weighed in addition to conventional considerations of time and memory usage. In some cases, the visual element can be the overriding consideration when choosing or developing an algorithm.

Computer science is a text-based discipline, both in terms of its central emphasis on mathematics fundamentals and algorithms, and in terms of the requisite programming skills. For this reason computer science faculty have little background that prepares them to look at a picture and analyze what they perceive. They have little experience in discussing or communicating about any visual aspect. Further, in most academic institutions, computer graphics is one course of a half-dozen that a computer science faculty may teach. The amount of time available to develop lectures or supporting materials for a computer graphics course is finite.

Art faculties also face a number of challenges. With visual literacy such an integral part of art education, it is curious why many in computer art do not address it. Most digital art classes come after the art fundamentals are completed and as a result, faculty in digital art may assume that the students already have a developed style of communication. Even if the fundamental course work covered ideas of visual literacy, the students need more experience and an understanding of how to apply it to the digital medium.

Another reason why visual literacy is not central to some computer art courses is because the material is often so technical that the lessons consume class time and energy, leaving little for interpretation and analysis. Although this is the case, technical instruction without visual literacy is void of meaning, thus communication. Even short lessons will positively impact the students’ visual literacy and not noticeably hinder their technical expertise.

In the context of traditional arts, computer graphics is relatively new and most art faculty were not trained in digital media, rather in other art disciplines. Because of this, many do not know some of the terminology that is part of the visual literacy in computer graphics. A standard taxonomy like the one presented in this paper will help the instructor understand some of the new vocabulary connected with digital visual literacy.

Finally, visual literacy is often skipped in digital art classes simply because the analysis and interpretation of meaning derived from visual imagery is often the most challenging part of making art. Visual imagery is even more ambiguous than language and the interpretation of artwork is hard to teach. Ambiguity is part of art and instructors can support multiple interpretations.

Visual Analysis

In order to impart a visual literacy to computer graphics students, the authors, one in art, one in computer science, use an interdisciplinary approach called visual analysis. This technique consists of the discovery of visual cues followed by identifying the algorithm associated with those cues. In addition to learning this language, students apply the terms to their own work when they present it during critiques. This approach owes much to critical analysis, which establishes a structure for examining works of art [2]. In critical analysis, students in the visual arts learn to describe and compare works in terms of design, concept and media.

Instead of design, concept and media, visual analysis teaches students to recognize a small number of visual cues, including:

  •  Visibility of polygon faces or polygon edges.
  •  Transition from light to dark on diffuse-reflecting surfaces.
  •  Color and shape of specular highlights.
  •  Presence of transparency, reflection, refraction, patterns and textures.
  •  Sharpness of shadow.
  •  Interactions between adjacent diffuse reflectors (color bleeding).

These cues are usually sufficient to identify a rendering algorithm as Table 1 demonstrates. Informal studies have shown that both instructors and students find this list of cues non-threatening and easy to spot in an image. By learning to observe and describe these cues, students are able to identify rendering algorithms.

1. First, determine the surface algorithm.
Outlined polygons. Object’s far sides are visible. Uninterrupted horizon lines.Wireframe
Outlined polygons. An object’s far sides are not visible. Occluded objects are invisible.Hidden-line removal
Filled-in polygons. Presence of refraction in transparent objects. (Presence of reflection, shadows are also helpful)Ray tracing
Filled-in polygons. No refraction, reflection or shadows.Z-buffer
2.Determine the shader.
For Z-buffer:
One color per object. Objects appear flat, as if cut from paper.Constant
One color per polygon. Objects appear to have a shaded contour.Faceted
Smooth transitions from light to dark. Specular highlights follow polygon edges.Gouraud
Smooth transitions from light to dark. Specular highlights are white, compact and elliptical. "Shiny plastic" look.Phong
For ray tracing:
Opaque, colored surface. No highlight.Diffuse
Opaque, colored surface with highlight.Diffuse and Specular
Transparent object that appears to bend light. "Made of glass."Transparency
Part of scene is visible in the surface of the object.Reflection
3.Determine additional surface interest (both z-buffer and ray tracing).
Image appears to have been pasted or glued onto object.2-D Texture Mapping
Object appears to have been carved from a solid substance like stone or wood.3-D Texture Mapping
Object surface appears rough or wrinkled, but its profile is smooth.Bump Mapping
4.Light sources.
Harsh shadows/lighter shadows.Low/high

Table 1.

Presentation in Computer Science Classes

Most computer science students have had no previous experience in looking and relooking at an image, so the teacher presents the taxonomy gradually during the first half of the course. In the first lecture, the teacher discusses images portraying three or four algorithms that produce starkly different effects. For example, the four surface algorithms of wireframe, hidden-line, ray tracing and z-buffer create distinctive effects and can be distinguished by the use of only three visual cues. Each week, the teacher adds more algorithms, and by the midterm, the class has examined all commonly used rendering algorithms. Until the midterm, the teacher emphasizes the characteristic visual behavior of the algorithms. After the midterm, the teacher shows how multiple algorithms can achieve equivalent effects. By the end of the course, students learn to identify the surface algorithm, shaders and types of light sources.

While discussing a visual cue, the teacher presents two or three images that demonstrate it. After explaining the visual cue, the teacher gives the name of the algorithm that creates it. The teacher then shows a short series of images and invites students to identify the visual cues and then suggest a rendering algorithm. Students should specify the cues first so that they learn to spot them in the context of an image. Any premature guesses as to the identity of the rendering algorithm are met with the response, "Cues first!" After the first class meeting, discussions on visual analysis begin with a short series of images that review the rendering algorithms from the previous meeting. Students first name the visual cues and then suggest a rendering algorithm.

The methodology requires only five minutes of an hour-long lecture. Such a small amount of time will not significantly impact the presentation of other topics, especially if the teacher covers visual analysis during the last five minutes, when student focus is beginning to wander.

Presentation in Art Classes

Because art teachers already incorporate various types of visual analysis in classroom settings, the suggested visual analysis associated with the digital medium can be woven alongside the beginning technical instruction. The students can then use it along with traditional art language to talk about the meaning and formal qualities of their own work during critiques.

A teacher in digital arts would take a somewhat similar approach to teaching visual literacy as the computer science teacher. For each technical lecture, the teacher will introduce the terminology associated with the visual cues as well as explain how different visual elements change the look, feel and interpretation of an image. The student will then interlace these terms and ideas into the critical analysis of their own work and that of their peers. In this way they are tying traditional critical analysis with the visual analysis presented in this paper.

The technical lectures are not limited to three dimensional (3D) computer graphics because art students focus in many areas within the digital realm including two dimensional (2D) imagery, digital installation or sculpture, and time-based works such as interactivity, 2D and 3D animation and non-linear video work. In discussing these areas, the teacher will introduce the terms relative to the different formal and conceptual aesthetics of that focus.

No matter the focus within computer graphics, traditional visual language can be mixed with the technical language. Some forms of critical analysis include the recognition of formal elements such as line, shape, contrast and color. Discussion of these aspects can detail distinctions that give these forms character, which in turn can effect the way the image is read. For example, a line can be heavy, light, dark, structured, graceful or organic. If the lines are light and organic, such as in William Blake’s drawings of Bible passages, the viewer tends to think of spiritual ideas. In contrast, lines in some of Jackson Pollock’s paintings from the 1950s are heavier, ungraceful and often hesitant in movement. These awkward lines could be read to represent the tensions of post WWII America, a kind of undulating expression of freedom laced with an uncomfortable feeling of change. Line is not limited to drawing and can be seen in all media including sculpture, mixed media and even dance. The same kind of in-depth analysis could be applied to ideas such as contrast, shape and color.

Coupled with the technical terms used in the visual analysis presented in this paper, students can learn how to talk about their ideas in a rich and descriptive way. For example, if a student chooses to make a work that has an organic spiritual feel, she or he would most likely shy away from Phong shading and opt for something like a Gouraud algorithm. The student would also stay away from harsh or sharply defined shadows such as those caused by point lighting and experiment with some kind of area or ambient light.

Both during the technical lecture and during the critiques, or the presentation of student work, the teacher will show the students how to fuse the technical terms with traditional critical analysis using a method similar to Feldman [3] or Arnheim [2]. In Feldman’s approach, the students first describe what they see, then associate what they see with a formal analysis or a relationship between the shape, color and form that they have already observed. From these data, the students will interpret the work, and finally assess or judge it.

It is important for the instructor to teach that cognition of visual imagery is made up of both perception and thinking, that is the gathering of visual information and the processing of it, in concert [1]. What an artist presents to the viewer, be it digital media or painting, is imagery that the viewer uses to think and ultimately understand the work. Visual literacy gives students the language to communicate about and critically analyze their work.


  1.  Arnheim, R. Visual Thinking, Berkeley, University of California Press, 1969.
  2.  Arnheim, R. Art and Visual Perception, Berkeley, University of California Press, 1974.
  3.  Feldman, E. Varieties of Visual Experience, Englewood Cliffs, NJ, Prentice Hall, Inc., 1967.
  4.  Lanier, V. Essays in Art Education: The Development of One Point of View, New York, MSS Information Corp., 1976.
  5.  Morie, J. F. "Training in Computer Graphics for Entertainment Production: What Future TDs Need to Know,"Computer Graphics 33(4), November 1999.
  6.  Public Affairs Coalition of the Alliance of Motion Picture and Television Producers. Making Digits Dance: Visual Effects and Animation Careers in the Entertainment Industry, Sunnyvale, CA, NOVA Private Industry Council, 1997.
  7.  Regan and Associates. A Labor Market Analysis for the Interactive Digital Media Industry: Opportunities in Multimedia, Sunnyvale, CA, North Valley Private Industry Council (NOVA), 1997.

Rosalee Wolfe obtained a masters of music from Indiana University before changing majors to earn a Ph.D. in computer science.  She is a NASA Fellow, was SIGGRAPH Technical Slides Editor in 1993 and 1995-97 and edited Seminal Graphics for SIGGRAPH 98.

She also authored the 1997 education slide set on mapping techniques, co-created the first B.S. in human-computer interaction (at DePaul University) and is currently Director of the Division of Graphics and Human-Computer Interaction in the School of Computer Science, Telecommunications and Information Systems at DePaul University.

Dena Eber
School of Art
Bowling Green State University
Bowling Green, OH 43403

Rosalee J. Wolfe
Department of Computer Science
AC 450
DePaul University
Chicago, IL 60604

The copyright of articles and images printed remains with the author unless otherwise indicated.