Computer Science – Learning
Scientific paper
Aug 2006
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006iauss...2e..83f&link_type=abstract
Innovation in Teaching/Learning Astronomy Methods, 26th meeting of the IAU, Special Session 2, 17-18 August, 2006 in Prague, Cze
Computer Science
Learning
Scientific paper
While many backyard stargazers take up the hobby just for fun, many others are attracted to it because of their keen interest in learning more about the universe. The best way to learn science is to do science. Happily, the technology available to today's amateur astronomers — including computer-controlled telescopes, CCD cameras, powerful astronomical software, and the Internet — gives them the potential to make real contributions to scientific research and to help support local educational objectives. Meanwhile, professional astronomers are losing access to small telescopes as funding is shifted to larger projects, including survey programs that will soon discover countless interesting objects needing follow-up observations. Clearly the field is ripe with opportunities for amateurs, professionals, and educators to collaborate. Amateurs will benefit from mentoring by expert professionals, pros will benefit from observations and data processing by increasingly knowledgeable amateurs, and educators will benefit from a larger pool of skilled talent to help them carry out astronomy-education initiatives. We will look at some successful pro-am collaborations that have already borne fruit and examine areas where the need and/or potential for new partnerships is especially large. In keeping with the theme of this special session, we will focus on how pro-am collaborations in astronomy can contribute to science education both inside and outside the classroom, not only for students of school age but also for adults who may not have enjoyed particularly good science education when they were younger. Because nighttime observations with sophisticated equipment are not always possible in formal educational settings, we will also mention other types of pro-am partnerships, including those involving remote observing, data mining, and/or distributed computing.
Stencel Robert E.
Tresch Fienberg Richard
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