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.

Item Type:	Refereed Conference Paper
Research Division:	Information and Computing Sciences
Research Group:	Artificial intelligence
Research Field:	Intelligent robotics
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Langan, G (Mr Gaurav Langan)
UTAS Author:	Montgomery, J (Dr James Montgomery)
UTAS Author:	Garg, S (Dr Saurabh Garg)
ID Code:	118096
Year Published:	2016
Deposited By:	Information and Communication Technology
Deposited On:	2017-07-04
Last Modified:	2018-02-01
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