Scissors, Papers Rock: Old-World Technologies for Future-Proofing Pedagogy. Re-engaging Students in Mathematics Classrooms

Chapter

Abstract

Students continue to reject mathematics when they have a choice, particularly in the senior school years and at tertiary levels (Australian Academy of Science, Mathematics and statistics: critical skills for Australia’s future. The national strategic review of mathematical sciences research in Australia, Australian Academy of Science, Canberra, 2006; Grootenboer P, Zevenbergen R, Identity and mathematics: towards a theory of agency in coming to learn mathematics. In: Watson J, Beswick K (eds) Mathematics: essential research, essential practice. Proceedings of the 30th annual conference of the Mathematics Education Research Group of Australasia, Tasmania, vol. 1. MERGA, Adelaide, pp 335–344, 2007). Furthermore, students continue to see mathematics as irrelevant, dull and of little practical value and so many complete their formal mathematics education with poor mathematical identities and feeling mathematically disenfranchised. Despite the hope that modern technologies would result in improved and more effective mathematics pedagogy and improve student interest in this field, student disengagement is a continuing problem. Taking a more open approach to the definition of technology than is often the case in mathematics classrooms, this chapter explores the potential mathematical investigations that make use of what might be thought of as old-fashioned technologies. We demonstrate the role these forgotten technologies play within an investigative approach designed to support the development of particular mathematical concepts for a group of at risk and disenfranchised learners and show how we can develop new relationships between kids and mathematics.

Keywords

Mathematical Knowledge Mathematical Concept Mathematical Learning Mathematical Practice Australian Academy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science +Business Media B.V. 2012

Authors and Affiliations

  1. 1.University of the Sunshine CoastMaroochydoreAustralia
  2. 2.Griffith UniversityGold CoastAustralia

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