Panel 4: What Should We Teach in a Scientific Visualization Class?

Scientific Visualization (SciVis) has evolved past the point where one undergraduate course can cover all of the necessary topics. So the question becomes "how do we teach SciVis to this generation of students?" Some examples of current courses are: A graduate Computer Science (CS) course that prepares the next generation of SciVis researchers. An undergraduate CS course that prepares the future software architects/developers of packages such as vtk, vis5D and AVS. A class that teaches students how to do SciVis with existing software packages and how to deal with the lack of interoperability between those packages (via either a CS service course or a supercomputing center training course). An inter-disciplinary course designed to prepare computer scientists to work with the "real" scientists (via either a CS or Computational Science course). In this panel, we will discuss these types of courses and the advantages and disadvantages of each. We will also talk about some issues that you have probably encountered at your university: How do we keep the graphics/vis-oriented students from going to industry? How does SciVis fit in with evolving Computational Science programs? Is SciVis destined to be a service course at most universities? How do we deal with the diverse backgrounds of students that need SciVis?