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Faculty of Accounting and Informatics

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    Intelligent decision support system for selection of Learning Apps to promote critical thinking in first year programming students
    (2021-12-09) Singh, Kesarie; Naicker, Nalen; Rajkoomar, Mogiveny
    The disruption on higher education across the globe through adverse events such as student strikes, natural disasters and pandemics like Coronavirus Disease 2019 (Covid-19), can have catastrophic long-term effects on its sustainability unless there are significant and innovative research endeavours to mitigate this impact. Never before has the desire to keep learners motivated, engaged and successful in advancing their knowledge and perfecting their 21st century skills through student-centred, technology-rich teaching and learning practices, become so imperative across disciplines and job profiles. In particular, the problem associated with teaching programming to novice learners is further exacerbated by the complex and abstract nature of the field and the heavy reliance on 21st century skills such as critical and computational thinking. As a result, a kaleidoscope of research into programming self-efficacy, the complexity of the field, teaching methods and a variety of teaching tools, have emerged over the recent past. In response, the aim of this research was to use decision support systems to obtain student-centred preferences for learning applications to promote critical thinking in first year programming students. This study focuses on the visual programming environment and critical thinking as the gateway skill for student success in understanding programming. The extensive literature review has revealed an array of learning Apps and a multiplicity of critical thinking criteria that serve a diverse set of needs and expectations. Therefore, research to develop a multiple attribute decision-making model is needed to assist academics make quick, scientifically-proven, accurate and collective decisions about which learning App to choose from the range of available alternatives. The study used decision theory and Diane Halpern’s 4-part model for critical thinking as the theoretical frameworks for evaluating and selecting learning Apps on the basis of its capacity to promote critical thinking. As a quantitative study, it randomly selected 217 students from a population of 500 programming students to rate four learning Apps, namely, Scratch, Alice, Blockly and MIT App Inventor, against critical thinking criteria and established Scratch as the App that best promotes critical thinking among first year programming students. Consequently, its distinctiveness lies in its use of the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) multi-criteria decision-making algorithm to rank criteria for critical thinking, calculate their weights on the basis of informed opinion and hence scientifically deduce the best rated App among the available alternatives that promote critical thinking among first year programming students. Furthermore, the study offers useful, insightful and ranked critical thinking criteria to formulate a user-friendly, transparent and evidence-based framework for App selection among academics teaching programming in higher education institutions.
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    Intelligent decision support systems for managing the diffusion of social computing in school-based ubiquitous learning
    (2022-01-06) Sam, Caitlin; Naicker, Nalen; Rajkoomar, Mogiveny
    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.
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    Examining perceptions of academic staff on the factors affecting the use of smartphones as a Constructivist Learning Tool : a proposed model
    (2021-07-05) Dyubele, Sithembiso; Heukelman, Delene; Soobramoney, Subashnie
    The rapid growth of mobile technology has brought enormous benefits in terms of communication and how some tasks may be accomplished using this technology. However, although many benefits have been identified, many disadvantages have also been identified. The focus of this study was to determine the perceptions of academic staff members on the factors that affect the use of smartphones as a constructivist learning tool by students rather than as a mere communication and distraction gadget. The factors that could affect the use of smartphones as a constructivist learning tool were identified through a comprehensive literature review. Based on the factors found, a model depicting the relevant factors was constructed, and the model was validated. Six independent constructs for the model; Demographics, Attitudes towards smartphones, Facilitating Conditions, Perceived Ease of Use, Perceived Usefulness, and Performance Expectations, were identified by grouping variables to measure each construct together. A questionnaire, based on the constructs and variables, was administered. The resulting data were analysed to validate the model. The conceptual model, tested by the survey, showing the significance of each factor, indicated that all the independent constructs impact the use of smartphones as a constructivist learning tool, either for communication and/or sharing academic-related activities. The results of this study found that Demographics, such as academic departments, Attitudes towards smartphones, Facilitating Conditions, Perceived Ease of Use, Perceived Usefulness, and Performance Expectations all impact the use of smartphones as a constructivist learning tool.
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    Developing an expanded Technological Acceptance Model for evaluating e-Learning in the Sub-Saharan African environment
    (2020-09-04) Ujakpa, Mabeifam Martin; Heukelman, Delene
    The Technological Acceptance Model was originally developed in the United States of America, which is culturally different, from Sub-Saharan Africa. Applying the existing Technological Acceptance Model to evaluate technological applications intended for the SubSaharan African environment, is likely to give inaccurate results because of the cultural dissimilarities and the diverse socio-cultural composition of Sub-Saharan Africa. As a way to improve accuracy of results, this research reviewed relevant literature and applied a mixed methodology to gather data from 308 students from five public universities in five countries across the five Sub-Saharan African regions (North, South, East, West and Central) on the use of e-learning in universities. Upon analyses of the data through Cronbach‘s α measure, Kaiser-Meyer-Olkin‘s measure, Bartlett‘s test of Sphericity, confirmatory factor analysis and descriptive statistics, an extension of the original technology acceptance model was developed. The extended model has seven constructs: Perceived Ease of Use, Perceived Usefulness, Perceived Performance, Perceived Benefits, External Factors, Behavioural Intention, and Technological Acceptance. Four of these constructs (Perceived Ease of Use, Perceived Usefulness, Perceived Performance and Perceived Benefits) directly influence Behaviour Intention. In consonance with previous findings in literature findings, Perceived Usefulness rated higher than Perceived Ease of Use. Perceived Benefit rated the lowest among the four constructs. The research further confirms previous findings that Perceived Ease of Use influences Perceived Usefulness. Additionally, this study found that External Factors directly influence Perceived Usefulness, Perceived Ease of Use, Perceived Performance and Perceived Benefit. Amongst these, External Factors influence Perceived Benefit most, followed by Perceived Ease of Use, Perceived Performance, and lastly Perceived Usefulness. Last, but not least, the research further found that Behaviour Intention influences Technological Acceptance positively. Considering that this research collected data from only five countries in Sub-Saharan Africa to develop and test the model, caution needs to be taken when generalising the research findings beyond the said population and technology considered in the research. Future research on technological acceptance may refine the suggested expanded model to explain further, the variance in students‘ Behaviour Intention, Perceived Ease of Use, Perceived Benefit, Perceived Usefulness and Perceived Performance and also to examine the performance of the suggested expanded model to explain the different technology acceptance behaviours in the information technology field