Text in English / Although research paradigms and philosophies are highly regarded as frameworks and guides for choices of methods, application thereof is not evident. One of the reasons for the relatively limited application is the complexity and understanding surrounding paradigms and philosophies, making it hard for scholars to determine their stances and implications. This study describes a model for automatically predicting peoples’ paradigm and philosophical stance, including meaning, and their impact on research by helping the user determine the paradigm and philosophical stance closest to their beliefs. Paradigm and philosophical attributes are automatically derived from a set of structured questions that use information matching techniques. The development of a model for Research Paradigm and Philosophy Index (RPPI) follows a two-phase approach. The first phase involves automatic extraction of key indicators from a composed database that utilises an indexing scheme with different philosophies and associated implications. The second phase applies a matchmaking technique that automatically draws information reflecting the user’s attribute. This type of technology exists, but mainly in the dating and career matching fields. None exists for research paradigm and philosophical stances. The prototype system was designed and implemented to serve as a proof of concept, and was developed in Angular, using the Visual Studio Code, and Java, using Eclipse. The database was created using MySQL. The prototype system was designed and implemented to serve as a proof of concept due to the Intellectual Property nature of the product. Usability testing was conducted and results show that the participants agreed the system was simple, straight-forward to use, quite user-friendly and easy to learn, with easy navigation through menu items. / Computer Science / M.Sc. (Computing)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/26158 |
| Date | 01 1900 |
| Creators | Mphahlele, Stanford Morore |
| Contributors | Mkansi, Marcia, Mnkandla, Ernest |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (ix, 70 leaves) : illustrations, graphs, application/pdf |
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