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Towards an Ontology-Based Approach to Knowledge Management of Graduate Attributes in Higher Education

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Knowledge Management and Acquisition for Intelligent Systems (PKAW 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7457))

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Abstract

Knowledge around graduate attributes (GAs) is an area in need of knowledge management strategies. GAs are the qualities and skills that a university agrees its students should develop during their time with the institution. The importance of GAs and ensuring they are embedded and assessed is widely accepted across higher education. This research paper uses Grounded Theory and Network Maps to gain insights into the issues of similarities and differences in the discourse around our sample universities. To cover these two perspectives, we had two researchers involved in data analysis, one with the goal of distilling key ideas and identifying similarities and the other with the goal of untangling and drawing out differences. There is no unified taxonomy of managing GAs. The motivation to create such ontology is to push the standardization process that will enable the connection among academic systems and improve educational workflows, communication, and collaboration between universities.

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Author information

Authors and Affiliations

  1. Department of Computing, Faculty of Science, Macquarie University, NSW, 2109, Australia

    Amara Atif, Peter Busch & Deborah Richards

Authors
  1. Amara Atif
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  2. Peter Busch
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  3. Deborah Richards
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Editor information

Editors and Affiliations

  1. Department of Computing, Macquarie University, 2109, North Ryde, NSW, Australia

    Deborah Richards

  2. School of Computing and Information Systems, University of Tasmania, 7000, Hobart, Tasmania, Australia

    Byeong Ho Kang

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© 2012 Springer-Verlag Berlin Heidelberg

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Atif, A., Busch, P., Richards, D. (2012). Towards an Ontology-Based Approach to Knowledge Management of Graduate Attributes in Higher Education. In: Richards, D., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2012. Lecture Notes in Computer Science(), vol 7457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32541-0_20

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  • DOI: https://doi.org/10.1007/978-3-642-32541-0_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32540-3

  • Online ISBN: 978-3-642-32541-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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