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Modeling astronomy education, the case of F-HOU tools : SalsaJ and Human Orrery

Published online by Cambridge University Press:  23 December 2021

Emmanuel Rollinde Open the ORCID record for Emmanuel Rollinde [Opens in a new window]
Emmanuel Rollinde*
Affiliation:
CY Cergy Paris Université, LDAR, F-95000 Cergy, France Universités de Paris, Artois, Paris Est Creteil, Rouen email: [email protected]
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Abstract

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This communication introduces two cases of the use of astronomy as a motivating context to interdisciplinary science education with emphasis on modeling activities. Firstly, a dedicated software, called SalsaJ, allows students to reproduce the same data analysis as made by astronomers. The case of exoplanet detection will be used as an exemple. Secondly, bodies of learners are considered to model movements of planets with a Human Orrery (a Spatio-Temporal Map of the Solar System), connecting thus mathematics, physics, geography and arts.

Keywords

methods: data analysissolar system: generalastronomy education
Type
Research Article
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S367: Education and Heritage in the Era of Big Data in Astronomy , December 2019 , pp. 53 - 56
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of International Astronomical Union

References

Blum, W., & Leiss, D. 2007, Mathematical Modelling. Education, Engineering and Economics-ICTMA, 12, 222–231.Google Scholar
Doran, R., Melchior, A. L., Boudier, T., Ferlet, R., Almeida, M. L., Barbosa, D., & Roberts, S. 2012, arXiv preprint arXiv:1202.2764.Google Scholar
Helding, B., Megowan-Romanowicz, C., Ganesh, T., & Fang, S. 2013, in Modeling Students’ Mathematical Modeling Competencies, 327–339, Springer, Dordrecht.Google Scholar
Hestenes, D. 2006, Proceedings of the 2006 GIREP conference: Modeling in physics and physics education, 31, 27. Amsterdam: University of Amsterdam.Google Scholar
Rollinde, E. 2016, International Journal of Science and Mathematics Education, 17(2), 237–252.CrossRefGoogle Scholar
Rollinde, E., Ferlet, R., Melchior, A. L., Delva, P., Chagnon, G., & Salomé, P. 2016, Le Bulletin de l’Union des Professeurs de Physique et de Chimie, 4(983), 469–496.Google Scholar
Rollinde, E., & Decamp, N. 2019, Journal of Physics: Conference Series, IOP Publishing, 1287(1), 012011.Google Scholar
Rollinde, E., Pennypacker, C., Doran, R., Darhmanoui, H., Handa, T., Kothari, K., Lewis, F., Robberstad, J., & Megowan, C. 2020, Futures of Education - GHOU 2020, Intern report, hal-03089436Google Scholar
Rollinde, E., Decamp, N., & Derniaux, C. 2021, Physical Review Physics Education Research, submittedGoogle Scholar
Sensevy, G., Tiberghien, A., Santini, J., Laubé, S., & Griggs, P. 2008, Science education, 92(3), 424–446.10.1002/sce.20268CrossRefGoogle Scholar
Tiberghien, A. 1994, Learning and instruction, 4(1), 71–87.10.1016/0959-4752(94)90019-1CrossRefGoogle Scholar
Yvain-Prébiski, S., & Modeste, S. 2020, Proceedings of the CIEAEM conference, 71, 139–150.Google Scholar