The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.
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.
The COVID-19 pandemic created the need for a global change in tertiary education. Universities that traditionally relied on contact with students in physical classrooms were forced to consider modes of remote teaching to mitigate the risks of infection due to physical proximity. This study evaluates the emergency remote teaching implemented within the Department of Information Technology at the Durban University of Technology, South Africa. An emergency remote teaching model with four stages consisting of: preparation, synchronous and asynchronous teaching and learning, e-assessments and reflections are described, analysed and evaluated with reference to both lecturers and students. The evaluation is performed using both qualitative and quantitative research methods. Qualitative analysis was performed on 29 sources using content analysis. 229 initial codes were identified and first categorized into 13 subcategories and finally to the four categories synonymous with the adopted four-stage emergency remote teaching model: preparation (135 references), asynchronous and synchronous teaching and learning (67 references), e-assessments (25 references) and reflections (8 references). Quantitative data on the use of the learning management system from 2019 to 2020 evaluated the results of the applied changes in practice. From the results, it was evident that students and lecturers invested much time in the learning management system with 13 tools being adopted by the 49 analysed subjects. The learning management system was used extensively for communication, assessment and dissemination of subject content. The comparative results of the data from the 2019 and 2020 academic years showed that the majority of the 2020 subjects’ final results were statistically higher than the 2019 results. Results of analysis revealed the success of the implementation of the four-stage emergency remote teaching model. Received: 29 June 2021Accepted: 26 April 2022
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