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Natural language processing for researchh philosophies and paradigms dissertation (DFIT91)

Research philosophies and paradigms (RPPs) reveal researchers’ assumptions and provide a systematic way in which research can be carried out effectively and appropriately. Different studies highlight cognitive and comprehension challenges of RPPs concepts at the postgraduate level. This study develops a natural language processing (NLP) supervised classification application that guides students in identifying RPPs applicable to their study. By using algorithms rooted in a quantitative research approach, this study builds a corpus represented using the Bag of Words model to train the naïve Bayes, Logistic Regression, and Support Vector Machine algorithms. Computer experiments conducted to evaluate the performance of the algorithms reveal that the Naïve Bayes algorithm presents the highest accuracy and precision levels. In practice, user testing results show the varying impact of knowledge, performance, and effort expectancy. The findings contribute to the minimization of issues postgraduates encounter in identifying research philosophies and the underlying paradigms for their studies. / Science and Technology Education / MTech. (Information Technology)

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/27471
Date28 February 2021
CreatorsMawila, Ntombhimuni
ContributorsMkansi, Marcia, Mnkandla, Ernest
Source SetsSouth African National ETD Portal
LanguageEnglish
Detected LanguageEnglish
TypeDissertation
Format1 online resource (xi, 195 leaves) : illustrations, application/pdf

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