B. Project DescriptionB3. Motivation So Why Focus on Numerical Models and Computational Science? For quite some time, computation played a subservient role in analyzing data from experiments or in evaluating various integrals or approximations from theory. Science can no longer be divided simply along the lines of experiment and theory. Computational science has arisen as a new way of doingscience, enabling observations to be made that are not possible in any other way. Computational science has been defined in many ways. We define it as the correct and efficient match of application, algorithm, and architecture which enables one to do science or engineering on a computer. Alternatively, we can say that computational science is any area of science or engineering in which the computer plays an essential role in the model building or solution. The advent of HPCC technologies to the research community over the last decade has revolutionized the way science and engineering are conducted; these same technologies have not yet had any significant impact at the undergraduate level. The Internet and Web have also brought access to a great deal of network-accessible data streams that pass themselves off as "information" and interactive learning environments, but little attention has been paid to assessing the quality of such materials. There is not yet a broad "computational culture" in the university or educational communities, a culture that encourages computational models while promoting the need to test the results of numerical investigations.  Mathematicians, scientists, engineers, and educators have long advocated a wider inclusion of authentic technologies enabling computational models and simulations for several reasons. First, since practicing mathematicians, scientists, and engineers use such models or simulations in ever-increasing numbers, their use at the undergraduate level would help close the gap between how science is done and how science is taught. Second, the use of such models, whose output is largely visual, enables the study of phenomena that are otherwise inaccessible to most students (for the same reason they would be inaccessible to most scientists) since the phenomena occur too fast, too slow, are too big or too small, or are too complex or dangerous for direct, hands-on examination in an undergraduate laboratory; examples include the changing of voting habits in various communities, effects of various atmospheric pollutants, the formation of galaxies, the dynamics of world economies, the erosion of beaches, or the modes of vibration in a molecule. Third, some have (naively) suggested that the use of technology and numerical models, solved and visualized by computers, would give students with poor or underdeveloped mathematical skills access to much of science and engineering since the calculator or computer (by means of a program or a computer-algebra system such as Maple or Mathematica) would "solve the problem for them." Our experience clearly demonstrates that the first two reasons to incorporate emerging technologies in education are validated if the technology is used in an authentic way, demonstrating its effectiveness for both the professor and student. The teachers are unlikely to use the technology in class if they are not using the technologies as part of their own professional development and practice; the students will see little value in the technology if used in class only, or if the professor cannot profess its practical utility for accessing, manipulating, interpreting and disseminating information from a variety of sources. However, as those who have seen students use (and misuse) a graphing calculator or personal computer or the Internet recognize, the third reason listed above for using technology to reach students who do not have an appropriate preparation for more traditional approaches in science and engineering education is nearly contradicted by direct experience. Rather than being able to use computers and numerical models to avoid teaching mathematical rigor, we find that technology-based approaches require the teaching of and motivates the learning of the mathematical foundations of the underlying calculations being performed. Students of today depend on visual stimulation and their inherent visual learning modes. Accordingly, we have observed that computational models and numerical simulations are so attractive that they can motivate students to understand the mathematical foundations that enable them to fully explore the models. And these models can motivate the undergraduate professor, as well! Computational science, besides being an essential component today to almost every science, therefore, can be an enabling component of an undergraduate curriculum: effective use of models and computation allows both teacher and student to go beyond closed-end, watered-down, abstract problems, to tackling real world problems instead. The combined experience of the Shodor Foundation and its partners in teaching summer institutes to undergraduate faculty and in training student interns convinces us that computational science truly can excite teachers and students about science, undergraduate research projects, graduate and undergraduate school, and careers in science, mathematics, engineering, and teaching. Why Focus on Undergraduate Faculty? Undergraduate faculty are of special concern to us for several reasons. In the first place, undergraduate faculty are teaching the future K-12 teachers, the future graduate students, and the future work-force. If we want to have the biggest impact on educational reform and improvement, undergraduate education is the pressure point. Secondly, research universities continue to advance in their use of computational science as a new methodology in scientific discovery and in their use of HPCC technologies. The gap between the predominately undergraduate institutions and the nation's research institutions is widening, yet across the country most graduate students still come from predominately undergraduate institutions, and a growing number of the four year institution graduates start in two year institutions. We would like to maintain and even increase the number of qualified students who apply to graduate school or who can transfer to four-year institutions, with a special emphasis on students who are underrepresented in the areas of science, mathematics, engineering, and education. To reach these students, we have to reach their teachers first. Among the barriers impeding undergraduate faculty from initiating or incorporating modeling or an effective computational science instructional program at their home institutions, we note three of the most frustrating: a) Computational science is a developing discipline. Most faculty at the undergraduate level are unfamiliar with numerical models and their origins in their own fields of expertise, and even less familiar in other disciplines. Computational science has not only changed the way sciences are studied, but it has also affected the very sciences that are being studied, even creating new sciences in the process. The SCSI activities are an effective means to bring faculty members the knowledge of new developments that are contributing to this new computational culture in science. This project will give the participants hands on experience in using models in doing computational science so that they will be more comfortable teaching with models. b) Computational science is inherently interdisciplinary. Faculty members need to work together in developing and delivering courses in modeling or which use modeling. Unfortunately, some university and college departments have an artificial hierarchy that works against collaborative efforts. In addition, we find that most faculty at the undergraduate level lack an appropriate knowledge base in computational science, so they are unaware of the need to correctly match the application, algorithm, and architecture to arrive at a correct solution. The team structured SCSI workshops provide faculty in the sciences, engineering, mathematics, computer science, and education with the interdisciplinary focus and skills they need to incorporate computational models in their courses or to develop fully interdisciplinary computational science courses . In addition, we expect that inter-institutional teams of faculty from the same discipline will be formed to tackle various problems. c) Computational science instruction generates computing and networking
requirements that strain the resources of most undergraduate institutions.
Faculty members with little or no computational experience will not be
able to utilize these resources in their instructional plans in effective
ways without training and support. The SCSI project educates and trains
faculty in the use of local and remote computing and scientific visualization,
in accessing resources over the network, and in identifying other network-accessible
resources that can enhance the undergraduate teaching experience.
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