An Investigation into Third Level Module Similarities and Link Analysis
| Autor: | Markus Hofmann, Michael Keane |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Knowledge management
Web content mining Operations research Higher education business.industry Computer science Teaching Educational systems Higher Education 03 medical and health sciences 0302 clinical medicine Web mining Substantial similarity Similarity (psychology) Web page Business intelligence Text visualisation Learning 030212 general & internal medicine Decision-making business Document similarity Network and link analysis 030217 neurology & neurosurgery Link analysis |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname |
| DOI: | 10.4995/HEAD17.2017.5528 |
| Popis: | The focus of this paper is on the extraction of knowledge from data contained within the content of web pages in relation to module descriptors as published on http://courses.itb.ie delivered within the School of Business in the Institute of Technology Blanchardstown. We show an automated similarity analysis highlighting visual exploration options. Resulting from this analysis are three issues of note. Firstly, modules although coded as being different and unique to their particular programme of study indicated substantial similarity. Secondly, substantial content overlap with a lack of clear differentiation between sequential modules was identified.. Thirdly, the document similarity statistics point to the existence of modules having very high similarity scores delivered across different years across different National Framework of Qualification (NFQ) levels of different programmes. These issues can be raised within the management structure of the School of Business and disseminated to the relevant programme boards for further consideration and action. Working within a climate of constrained resources with limited numbers of academic staff and lecture theatres the potential savings outside of the obvious quality assurance benefits illustrate a practical application of how text mining can be used to elicit new knowledge and provide business intelligence to support the quality assurance and decision making process within a higher educational environment. |
| Databáze: | OpenAIRE |
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