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Title: Intelligent decision support systems for managing the diffusion of social computing in school-based ubiquitous learning
Authors: Sam, Caitlin 
Keywords: Economic inequalities;Social media applications;Ubiquitous learning (u-learning)
Issue Date: 6-Jan-2022
The past decade has seen an explosion in social media applications. Most adolescents in South
Africa have access to social media applications despite the country’s economic inequalities.
The drive for social media applications is important to enhance human connectedness. In
unprecedented times social computing can be utilised in school-based learning to benefit
learners. Climate change has propagated extreme weather patterns which has increased the
occurrence of natural disasters and diseases. The emergence of the novel Coronavirus resulted
in most countries implementing nation-wide forms of lockdown to curb the spread of infection.
Consequently, these adverse phenomena across the globe are disruptive to conventional schoolbased education. Ubiquitous learning (u-learning) relates to learning that occurs at any place
without time constraints. In some schools, u-learning has become a conventional learning
approach and pedagogy but there are various education and technology attributes that must be
addressed for the penetration of social computing in schools. Therefore, there is a need to guide
learners and school-based instructors on their preferences of digital access and social media
applications. The main aim of the study was to investigate social media-driven Intelligent
Decision Support Systems using live data, to assist instructors and learners manage the
diffusion of social computing in school-based ubiquitous learning. In pursuing this study, a
quantitative research methodology was used for the collection of data from learners and
instructors from the schools in the eThekwini Region, namely, Umlazi District and Pinetown
District of KwaZulu-Natal Province, South Africa. A survey was conducted to elicit data from
participants on their use of social computing for u-learning. The approximate target population
size was 129 421 individuals with a sample size of 384 participants. There were 260
respondents with an acceptable response rate of 67,71%. The study derived attributes for
ranking the social media applications and Principal Component Analysis which is an
unsupervised Machine Learning algorithm reduced the dimensionality of the attributes. The
multi-criteria decision-making algorithm, Fuzzy Technique of Order Preference Similarity
Ideal Solution was implemented to rank the social media applications in line with the
dimensionality reduced criteria based on the subjective decisions of expert decision makers.
Data Envelopment Analysis, another multi-criteria analysis method was utilised to score the
efficiency of the devices for u-learning. The results showed the most precise, mathematically
approved social media applications and devices that can support u-learning in schools. An
automated application based on research evidence using Intelligent Decision Support Systems
was designed as a research output.
A thesis submitted in fulfillment of the requirement for the Doctor of Philosophy in Information and Communications Technology, Durban University of Technology, Durban, South Africa, 2021.
Appears in Collections:Theses and dissertations (Accounting and Informatics)

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