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Big data are referred to bulk data which cannot be collected, saved, and analyzed with the traditional data analysis tools. The field of learning analysis, which has consistently appeared on the Horizon Report from New Media Consortium for the recent years, is receiving fresh attention with the proliferation of big data. The purpose of this study is to examine the environment for the learning analytics, a branch field of big data, to be applied to the Korean education curricular and its possibility and find out how to improve them. First, as an application of a new technology involves side effects in most cases, it is desirable that potential problems be considered from the beginning and negative effects be minimized. Second, the learning analytics begins with securing sufficient data, and a data exchange system should be established for setting up the data ecology. Third, as big data are applied to public data and the corporate business areas and frequently mentioned in the media, the expectation for their potential growth is reaching its peak. [ABSTRACT FROM AUTHOR]

The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms. [ABSTRACT FROM AUTHOR]