Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

Purpose: The purpose of this paper is to identify and present a global perspective of digital pedagogies in relation to technology and academic librarians. Design/methodology/approach: The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was used in this study. Findings: Based on the data, academic librarians must develop a foundational understanding of 21st century pedagogies and digital skills to teach in an online environment. Originality/value: This review paper considers the emergent teaching role of the academic librarian within the digital environment. The themes in the findings highlight the importance of digital pedagogical knowledge and digital fluency of academic librarians as a teacher within the digital environment in higher education.

Energy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC‐Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC‐Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy‐efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC‐Gauss). The results show that the proposed DEEC‐Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.

Purpose: This study aims to evaluate faculty research repositories used in higher education institutions, their different levels and functions with regard to research information management. This is revealed through the selected studies reviewed. Design/methodology/approach: A systematic literature search of journal article studies on research repositories in higher education institutions was carried out on several databases, namely, Ebscohost, Emerald Insight, Science Direct, Sage, Google Scholar, SA e-Publications and citation databases such as Scopus and Web of Science. The systematic review was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses guidelines. The time frame for the analysis was 2015 to 2021. Findings: The findings are presented on the motives for developing faculty research repositories the services provided and benefits derived from faculty research repositories and what is the utilization of faculty research repositories. Originality/value: The results show that the development of research repositories at the faculty level enhances sharing, analysis, evaluation and preservation of scholarly research produced.

The economic impact of carbon emissions in Africa is gaining traction in the extant literature. This study adopted Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to concomitantly track data on carbon emissions versus economic growth in Africa from 2018 to 2022 providing evidence from a meta-analysis. Through database searches, 591 publications were identified. A machine learning algorithm called Latent Dirichlet Allocation (LDA) was used as a visualization technique for reporting trends in the eleven papers selected for the analysis. Identifying, evaluating, and summarizing the findings of all relevant individual studies conducted in Africa on the impact of economic growth on carbon emissions contributes to the existing body of knowledge. This study fills a critical gap by surveying the studies conducted in Africa in the last five years, implying that economic growth negatively and significantly triggers CO2 emissions in Africa. The debate on the economic impact of CO2 emissions in Africa, the most vulnerable continent to climate change, is elucidated. The findings tracked sources of data for carbon emissions in Africa. The results showed that although some studies reported a positive correlation (and some a negative correlation) between economic growth and carbon emissions, most studies concur that the economic impact of carbon emissions over a timeline can be explained by the Environmental Kuznets Curve (EKC) hypothesis. Therefore, there is a dire need for African countries to strengthen economic growth without deteriorating their environment or having ecological footprint. Future research must assess whether this trend on the economic impact of carbon emissions in Africa continues. AcknowledgmentThe authors express their appreciation to the Durban University of Technology for providing the resources to conduct this study.