2000 Physics Department Chairs Conference

April 15, 2000

Harvey Gould

Clark University

collaborator: Jan Tobochnik, Kalamazoo College

support: National Science Foundation

Viewgraphs: Punchlines, Computer Simulation Laboratory, Which Programming Language?, Gordon Research Conference, Statistical and Thermal Physics Curriculum Development Project, Resources

- Changes in the curriculum and innovations in educational methods should be guided by developments in research.
- Computation has led to important conceptual advances and new ways of thinking about physical systems which should be reflected in the curriculum.
- Our goal should be to incorporate computational methods into the curriculum rather than computers in the classroom.
- The most efficient way to introduce computational physics into the undergraduate curriculum is in a separate course.
- Computer simulations rather than numerical methods should be emphasized.
- If students have the needed skills, they will help reform the curriculum.
- Computing is not a substitute for thinking. Computational physics does not yield instant gratification as found in many other computer applications. We need to provide students opportunities to learn that computers do not lessen the need for thinking deeply and that such thinking has its own rewards.

rather than

Example of a course that emphasizes simulation:

- Computer simulations provide a opportunity of doing physics closer to the way research is done.
- Numerical methods more meaningful when part of a simulation than when taught only as a tool.
- Computer simulations encourage a broader vision of physics than is usually seen in undergraduate courses. Students can study models of interest to geologists, biologists, materials scientists, and social scientists. Course can attract a wide range of students.
- Simulations provide a way of reaching a deeper understanding of fundamental physical concepts, particularly by writing programs with graphics.
- Project oriented, minimum background required.
- Approach close to laboratory experiments.
- Students learn programming skills in context of physics.
- Simulations allow open-ended questions and encourages creative thinking in contrast to memorization and routine problem solving.
- The course at Clark involves undergraduate majors in physics, computer science, chemistry, mathematics, biology, and economics and graduate students in physics and chemistry. There is little correlation between the students' background and how well they do in the course. Our experience is that the earlier students take such a course, the better. An excellent example of a first-year course is taught by Wolfgang Christian at Davidson College.

- Difficult to add course to curriculum.
- Laboratory course open-ended and time consuming.
- Possibility of neglect of analytical skills (not observed in practice).

- Platform independent, inexpensive, and easy to learn.
- Intrinsic graphics statements.
- Libraries for numerical calculations.
- Modular and preferably object-oriented.
- Easy event-based programming capability.
- Useful outside of physics so that language will be maintained and improved and provide a marketable skill for students.
- Bit manipulation capability.
- Parallel programming capability or easy route to a language that does.

Thermal and Statistical Physics

- First in a series of conferences on how research in physics and research in physics education can be used to improve the teaching of undergraduate physics.
- The first conference, June 11--15, 2000, will be on thermal and statistical physics.
- Goal is to bring together workers who are active in research in thermal and statistical physics, researchers in the new field of physics education, and people who teach courses in statistical and thermal physics.

Advertisement: Gould and Tobochnik NSF sponsored project to enhance the upper division curriculum in thermal and statistical physics.

Most important goal: Develop a community of teachers and students to generate course materials and exchange ideas in an open source environment. Examples of topics:

- Nature of probability.
- Approach to equilibrium and increase of entropy.
- Microcanonical simulations (molecular dynamics or demon). Compute subsystem probability to motivate Boltzmann probability.
- Compare different ensembles by doing Monte Carlo simulations and molecular dynamics. Compare time averages to ensemble averages.
- Quantum Monte Carlo with noninteracting particles to understand better the significance of indistinguishability.
- Use broad histogram method to improve understanding and significance of density of states.
- Random walks and diffusion.
- Maximum entropy and image enhancement.
- Traffic flow.

- The SiP Web site has links to almost all of the available texts on computational physics, courses at Clark, Kalamazoo College, and other colleges and universities, and program listings from our text on computer simulation. Also available are tutorials on F (Fortran 90), True Basic, and Java (very preliminary draft).

Please send comments, additions, and corrections to Harvey Gould, hgould@clarku.edu.

Updated 21 April 2000.