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Augmenting Teaching

  • Kieron Sheehy
  • Rebecca Ferguson
  • Gill Clough
Chapter
  • 251 Downloads
Part of the Palgrave Macmillan’s Digital Education and Learning book series (DEAL)

Abstract

As we have seen, many of the positive features of augmented reality are not unique to technologically mediated learning. Nevertheless, augmented reality may offer several advantages to learners when used as part of an appropriate pedagogy and within particular topic areas. A systematic review of augmented reality noted the use of the terms “game-based learning,” “participatory simulations,” “problem-based learning,” “role playing,” and “jigsaw method” (Wu et al., 2013), reflecting both constructivist and socioconstructivist assumptions about teaching with augmented reality. They categorized this variety in terms of the most salient features of these approaches: engaging learners in roles, emphasizing learners’ interactions with physical locations, and also emphasizing the design of learning tasks. However, the types of pedagogy and models of learning that are explicitly mentioned in educational research related to augmented reality are varied and some do not explicitly report on their underpinning assumptions about how students learn (FitzGerald et al., 2012). Indeed, it has been argued that users of augmented reality may emphasize the entertainment of individual learners at the expense of pedagogical value (Initiative, 2005). Making use of augmented reality solely because it is a novel technology, rather than selecting it to achieve a pedagogical purpose, may undermine its educational value. In this chapter, we look at examples of the use of augmented reality within education and consider their educational merit, drawing on concepts developed in chapter 2 in order to assess their merits within educational practice.

Keywords

Augmented Reality Virtual Object Subject Knowledge Traditional Game Mented Reality 
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

© Kieron Sheehy, Rebecca Ferguson, and Gill Clough 2014

Authors and Affiliations

  • Kieron Sheehy
  • Rebecca Ferguson
  • Gill Clough

There are no affiliations available

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