Repository logo
 

Faculty of Accounting and Informatics

Permanent URI for this communityhttp://ir-dev.dut.ac.za/handle/10321/1

Browse

Subject: search.filters.subject.Expert systems (Computer science)

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Evaluating the level of satisfaction in higher education students with technical support services provided using fuzzy TOPSIS decision method
    (2024) Pursan, Geeta; Adeliyi, Timothy T.; Joseph, Seena
    The use of information and communication technologies at higher education institutions is no longer an option, but rather a need. Information Technology support is an essential factor that entails giving end users assistance with hardware and software components. Technical support for information technology has been recognized as a crucial element linked to student satisfaction because it helps students understand, access, and use technology efficiently. IT technical support services are essential for higher education students to succeed in their studies. However, the quality of IT technical support services can vary widely from institution to institution. Student satisfaction with IT technical support services is an important measure of the quality of education that students receive. Conversely, evaluating student satisfaction is a complex task, as it involves subjective assessments of service quality. This dissertation used a framework that combines three approaches: Principal Component Analysis (PCA), Service Quality (SERVQUAL), and Fuzzy TOPSIS. The successful implementation of IT technical support is aided by identifying the essential success criteria that enable efficient and effective support for students and instructors. Hence the main aim of this study is to identify and rank the key success factors for the successful implementation of IT technical support at higher education institutes. 81 key success factors identified from 100 research papers were analyzed using principal component analysis. The findings led to the identification and ranking of 25 PCs. From these findings, the SERVQUAL dimensions that featured at the top-most rankings were selected, and that being: tangibility, reliability, assurance, empathy, and responsiveness. These factors were used in the development of the questionnaire that was sent to students which measured student perceptions of the five dimensions of service quality. The proposed approach is implemented in a higher education institution in South Africa. The questionnaires were administered to a specific target of students, only those student participants’ who had contacted the IT technical team for IT technical support via the WhatsApp service communication method formed part of the study. Once data was collected, SERVQUAL which is a well-established scale for measuring service quality was used to calculate the average score for each dimension of service quality. The dimensions of service quality where students were most and least satisfied were identified. Finally, Fuzzy TOPSIS, which is a multi-criteria decisionmaking (MCDM) method that handles uncertainty and vagueness in data was used to rank the IT technical support services based on student satisfaction. The SERVQUAL results showed that the overall satisfaction level of students with IT technical support services led to a final score of 60 percent, meaning that the support services rendered were acceptable to students. The Fuzzy TOPSIS rankings identified the sub-criteria, overall being satisfied with the support services rendered as rank number one. As can be deduced that since both the SERVQUAL and Fuzzy TOPSIS methods have nominated satisfaction level as the common factor, this research indicates that the IT technical support services rendered by the IT technical support team are adequately sufficient and that the needs of the students are met and that the services rendered are highly appreciated by the students at the Durban University of Technology. This research proves that the IT support team is compliant with the quality of IT technical support services rendered to students at the Durban University of Technology however, the IT support service can be improved by the proactiveness of the technical team. This research contributes by providing useful information highlighting factors that can be used to examine areas in educational institutions that need to receive continuous and special care to generate high student satisfaction; ensure future success and gain a competitive advantage. These factors can assist the management of HEI in determining the success or failure of an institution in terms of the technical support provided to students and student satisfaction. The results of this evaluation can be used by other HEIs to improve the quality of IT technical support services and to ensure that they are meeting the needs of students.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.