Teacher and Student Research Using Large Data Sets

One of the objectives of teacher research experiences is to immerse the teacher in an authentic research situation to help the teacher understand what real research is all about: "to do science as scientists do." Experiences include doing experiments in laboratories, gathering data out in the field, and observing at professional observatories. However, a rapidly growing area of scientific research is in "data mining" increasingly large public data archives. In the earth and space sciences, such large archives are built around data from Landsat 7, the Sloan Digital Sky Survey, and in about seven years, the Large Synoptic Survey Telescope. The LSST will re-photograph the entire night sky every three day, resulting in a data flow of about 20 terabytes per night. The resulting LSST archive will represent a huge challenge of simple storage and retrieval for professional scientists. It will be a much greater challenge to help K-12 teachers use such gargantuan files and collections of data effectively in the classroom and to understand and begin to practice the new research procedures involved in data mining. At NOAO we are exploring ways of using large data sets in formal educational settings like classrooms, and public settings like planetariums and museums. In our existing professional development programs, such as our Teacher leaders in Research Based Science Education, we have introduced teachers to research via on-site observing experiences and partnerships with active astronomers. To successfully initiate research in the classroom, we have found that teachers need training in specific science content, use of specialized software to work with the data, development of research questions and objectives, and explicit pedagogical strategies for classroom use. Our research projects are well defined, though not "canned," and incorporate specific types of data, such as solar images. These data can be replaced with new data from an archive for the classroom research experience. This is already a form of data mining that can be applied to large data sets. We are looking at ways to apply our experience with hands-on observation experiences to the relatively abstract world of data mining. We are also looking at ways to move beyond the well-defined application to training teachers to develop their own more open-ended research activities. NOAO is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under cooperative agreement with the National Science Foundation.