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Threshold concepts framework: An effective approach to address future spatial sciences pedagogical challenges
Conference paper   Open access   Peer reviewed

Threshold concepts framework: An effective approach to address future spatial sciences pedagogical challenges

Sanjeev K Srivastava
Papers and Presentations of Queensland Surveying and Spatial Conference 2012, pp.1-16
Queensland Surveying and Spatial Conference (QSSC): The future of surveying and spatial science is Open, 2012 (Brisbane, Australia, 13-Sep-2012–14-Sep-2012)
Spatial Sciences Institute
2012
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Abstract

Geomatic Engineering learning concepts threshold concepts spatial science education
Over past decades, spatial technologies have become easy-to-use enabling citizens to use hardware and software tools without a good understanding of underlying concepts. Moreover, the World Wide Web is full of open-source software tools that can address a wide range of spatial issues. These developments are encouraging an increasing number of people to (mis)use these tools across multiple discipline areas. To harness the complete utility of modern spatial technologies, it is essential that the users have a sound conceptual understanding of the tools they are using, and this can only be achieved by utilising the latest advances in education research. Recently, a new learning hypothesis, threshold concepts, was proposed that provides conceptual gateways in a discipline-specific framework to transform learners from novice to expert. This framework draws eclectically on a number of theoretical perspectives both from different learning theories, and the ways students learn across a diverse range of disciplines. Considering the characteristics of threshold concepts, this paper identifies key threshold concepts and their interrelatedness with other concepts in spatial science sub-disciplines that include surveying, geographical information systems, and remote sensing. The identified concepts are: map scale and interoperability for all three sub-disciplines; data models for GIS and remote sensing; image analysis algorithms for remote sensing; and datums and surveying computations for surveying.

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