Computer Science is situated in this upheaval, while at the same time undergoing its own crisis. Is our discipline beholden to rapid changes in technology? Are our curricula based on outdated assumptions and criteria of what computer science is and what a computer science education should entail? Can we develop symbiotic industrial relationships without sacrificing academic integrity? Can we cope with an increasing need for interdisciplinary collaborations, both professionally and pedagogically?

By and large, students in our courses will not become computer scientists just as most students taking economics courses will not become economists and most students enrolled in mathematics courses will not become mathematicians. We must develop a core curriculum that serves computer scientists well, but that offers students with expertise in other disciplines the opportunity to either formally minor in computer science or to integrate the core of computer science into their major discipline. The time is ripe for this, we must not squander the opportunity that the Internet and the World-Wide-Web have provided for computer science by increasing both visibility and demand for those trained in our discipline.

Goals

Many of our peers look to the ACM, IEEE, and others for guidance only to find signposts leading to a maze of twisty passages, all of which look alike. Somehow we as computer science educators must find a way to pull together, towards some common priorities and objectives, rather than each (group) of us pulling in our favorite direction.

Priorities

• Despite the fact that computer science is not equivalent to programming, we must not forget that it is central to our enterprise as educators. Hoare puts this well:

  Having surveyed the relationships of computer science with other disciplines, it remains to answer the basic questions: What is the central core of the subject? What is it that distinguishes it from the separate subjects with which it is related? What is the linking thread which gathers these disparate branches into a single discipline? My answer to these questions is simple --- it is the art of programming a computer. It is the art of designing efficient and elegant methods of getting a computer to solve problems, theoretical or practical, small or large, simple or complex. It is the art of translating this design into an effective and accurate computer program. This is the art that must be mastered by a practising computer scientist; the skill that is sought by numerous advertisements in the general and technical press; the ability that must be fostered and developed by computer science courses in universities.

  The language used in our courses is not as important as how the language is used. Our first courses must provide students with infrastructure so that reasonably complex programs can be developed by using class libraries and augmenting existing code. We must do this in the context of reasoning as computer scientists so that students worry less about what kind of loop to use and more about why the system supplied sort function isn't appropriate. Students must learn to build programs from existing components and learn how to build the components as well.

• Developing a curriculum based on re-using existing code and classes requires a large base of existing code and classes designed for both introductory and advanced students across the undergraduate curriculum. This is a time-intensive enterprise. We must develop a national and international repository of pedagogical frameworks and infrastructure. The amount of wheel-reinventing done in most programs is shameful, wasteful and scandalous. The web makes this enterprise realizable, we must start immediately and we must procure funding.

• Theory and practice must be integrated throughout the curriculum. The algorithms course should include an experimental component so that students learn how realistic data sets, modern memory hierarchies, and hidden constants affect rough, worst-case big-Oh analysis. Courses in software design must emphasize that the right choice of algorithm or data structure is as important as the right widget-set.

• To make room for new courses, e.g., in networks and graphics, some courses will need to change. If there is agreement that courses must be removed or coalesced, we should look for ways to integrate material, perhaps through some kind of spiral approach, rather than removing courses wholesale. For example, integration of theory and practice can be combined with modern software toolkits in integrating courses in automata theory and compiler construction.

• We must listen to our industrial colleagues and work to integrate their needs into our framework. We literally cannot afford to ignore these needs. Perhaps the technology-transfer that has traditionally been from university to industry will begin to flow in both directions.

• We are forced to re-tool as technology changes. Although the core of computer science is independent of technology, our introductory courses are forced to track technology or lose students. Is this true of all courses? Is this our educational destiny? If so, we must provide resources and time for those involved in the delivery of courses and in the development of pedagogical tools.