1 What to Teach? Computational Science as an Improved Model for Science Education (Submitted 29 March 2008 to Microsoft Research RFP “Computational Education for Scientists – What to Teach?”) Rubin H Landau Director, Computational Physics for Undergraduates Program (CPUG) Oregon State University, Corvallis, Oregon 97331 [email protected], http://www.physics.oregonstate.edu/~rubin Abstract It is evident that computation has fundamentally changed research and development in most every field of science. This paper argues for concordant changes in science education that extend beyond using computers to teach traditional science better. Results will be presented from surveys of existing computational science degree programs that reveal a consensus of topics essential for the computational education of scientists. Detailed concept maps will be given, showing how educators are combining computer science and applied mathematics with a traditional discipline in order to teach how to solve realistic problems. It is proposed that teaching based on this research‐like, problem‐solving approach is a more motivating and efficient technique than teaching the various disciplines separately. Because it may be difficult for a single college or university to offer all of the essential topics, a number of early developers are placing eLearning modules from their courses together as part of a national repository. Figure 1. Computational Science & Application science Engineering (CSE) as a multidisciplinary endeavor connecting computer science with mathematics and applications. In this C view CSE is more than just the S overlap of traditional disciplines. CS E hard/software Changes in Science Drive What and How to Teach The historically rapid adoption of computation as an essential element in all areas of science and engineering has progressed faster than the concordant changes in education. Although many traditional science disciplines are incorporating computers to enhance education in those disciplines, the education too often presents computation as a “black box” whose inner workings need not be understood. (Some of the published evidence of this comes from Winch’ survey’s finding that few classes that incorporate computation as a course element actually examine the students about it [Winch].) Given the newfound and fundamental importance of 2 computation in so many disciplines, and the fact that many graduates of the traditional disciplines actually end up employed as computational scientists (proof to follow), we propose that students and society as a whole would be better served if the traditional science disciplines taught an understanding of computation as part of their science. In this paper we use the terms “computational science” or “CSE” to denote the multidisciplinary combination of techniques, tools and knowledge developed in the 1970’s and 1980’s to solve scientific and engineering problems through computer simulation. In terms of disciplines (Figure 1), it is a combination of mathematics and computer science with a field such as physics, chemistry, automotive engineering, etc. In our view, there also needs to be a central core (inner circle) based on a common toolset and mindset that draws the disciplines together. Teaching CSE encompasses the integration of modern research tools and subjects into undergraduate education, integration that US News & World Report considers a hallmark of a high quality education. Much of this paper will detail those subjects and tools. Figure 2. Importance of knowledge areas for physics bachelors, 5‐7 years after graduation. The light (blue) bars are for graduates whose primary field of employment is engineering, mathematics and science; the dark bars are for graduates employed in software. The Need for Computational Education It is wise to look in the cupboard before making up your shopping list. Likewise, it is prudent to look at what subjects are currently taught and what subjects students need after they graduate, before deciding to change your curriculum. We start by looking at the results of a survey of physics bachelors conducted by the American Institute of Physics that determined which aspects of their education are most valuable in their current employment five years after graduation [AIP]. The results, shown in Figure 2, indicate that for graduates whose 3 primary field of employment is engineering, mathematics and science, the three most important skills are scientific problem solving, synthesizing information, and mathematical skills. These skills are also highly important for graduates who find employment related to software. While it is to be expected that knowledge of software and programming are most important for graduates in software development, notice how, otherwise, synthesizing information is the most important skill for both groups, and that knowledge of physics is essentially the least important. (And these numbers come from the AIP!). Figure 3. Time spent on activities (top blue bar) compared to rating of education (lower red bar). The categories are team work skills, lab skills, scientific software, and programming. That same survey also examined (Figure 3) the importance graduates give to various activities as compared to their education in these activities. We see that while physics departments appear to be spending a proper amount of time on lab skills, their student might benefit if there was more time spent on scientific software, programming, and team skills. Although we have focused on physics here, there is evidence that similar observations hold in other fields. For example, a National Science Board report [NSB] indicates that only 22% of physics and biology undergraduates remain in the field from which they graduate, with the number rising to only 52% at the graduate level. To get an idea of just where they go, look at Figure 4, which shows the fields of employment for the physics bachelors of 2003 and 2004. Notice on the left that most bachelors (53%) are employed by the private sector, with only 4 12% going into colleges and university. Notice on the right that of those bachelors employed by the private sector, about one third assume non‐science, technology, engineering, or mathematics positions, about one third do engineering work, and about one quarter do technology work. This leaves only 13% to do physics or some other science. The conclusion we draw from these figures is that while a traditional disciplinary may provide good preparation for a student’s career, overemphasizing the specific discipline tends to weaken the preparation. Figure 4. Left: the initial employment sectors for the physics bachelors of 2003 and 2004. Right: The fields of employment for those graduates employed in the private sector. Now that we have documented the need for computation in science education, the next step is to determine if that need is being met. Figure 5 shows the results of a survey of the curricula of undergraduate programs in computer science (CS), physics (PH), computational science (CSE), and computational physics (CP) [Y&L]. The survey made the, admittedly crude, categorization of courses as Computing, Mathematics, Applications, and Other (whatever is left), and then displayed the results as the average percentage of the total curriculum dedicated to each category. The left column in Figure 5 shows the strong Computing (black) but weak Application (white) components in the Computer Science degree; the right column shows the strong Application but weak Computing components in the Physics degree. The middle columns show that the Computational Physics and Computational Science programs tend to provide a similar, uniform balance among Math, Computing and Application. So while these computational degrees provide less physics than a physics degree and less computing than a CS degree, the balance may be healthier. These numbers agree with our impression (prejudice?) that regular physics undergraduates do not learn enough about computation, and that regular CS undergraduates do not learn enough about math and science (there is of course much math in some CS classes). In summary, there appears to be a demand for mathematics, computational, and problem‐solving skills in the work place, as well as for providing a science education that can be applied to a variety of fields of employment. 5 Whether individual disciplines, with the natural pressure to get students to understand subjects the way the professors do (and thus teach even more of the discipline), can provide a proper computational education is unclear. However, computational science is still a young and developing field with no set curriculum, and I believe that it will take groups of interested parties to help decide what topics should be taught, and in which courses they may be included. Figure 5. (Bottom up): average % of classes taken in Computation, Math, Application, and all Other subjects for majors in (left to right) CS, CSE, Computational Physics, and Physics. Present Computational Education Programs For the past 25 years, individual graduate students have combined courses in mathematics, computer science and a traditional discipline in an ad hoc approach to obtaining a computational education. In that time, computational science has continued to mature, as has the power and pervasiveness of computers in science. This has led to formal degree programs at the graduate level, and more recently at the undergraduate level. Nevertheless, a bachelor's degree in any of the computational sciences