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Similarity Matching of Computer Science Unit Outlines in Higher Education

  • Gaurav Langan
  • James MontgomeryEmail author
  • Saurabh Garg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9992)

Abstract

With the globalisation of education, students may undertake higher education courses anywhere in the world. Yet there is variation between different universities’ offerings. Even though web search engines can help one to locate potentially similar courses or subjects offered by different universities, judging the degree of similarity between each of them is currently a manual process in which a student or staff member has to go through subject/unit descriptions within a course to understand the different topics taught. In this paper, we study the application of text mining to evaluate the similarity or overlap between different units and propose a system that can help students and staff to make these judgements. The unit or course descriptions are generally short, containing 100–200 words, and exhibit very wide diversity in the ways they are written. Experimental results using data from Australian and international universities demonstrate the accuracy of the proposed system in calculating the similarity between different computing units.

Keywords

Similarity Score Semantic Similarity Semantic Network Keyword Extraction Educational Data Mining 
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 International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Engineering & ICTUnversity of TasmaniaHobartAustralia

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