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Exploring the Research on Utilizing Machine Learning in E-Learning Systems
Summary
Not relevant to microplastics — this systematic literature review surveys how machine learning techniques are applied in e-learning systems to improve educational outcomes and predict student performance.
Naturally, you are already familiar with the phrase "E-Learning" in a time when information technology rules and everything is digital. electronic learning, e-learning. Through e-learning, anyone, at any time, can participate in the teaching and learning process. Distance and time are no longer impediments to completing activities, including learning in this situation, just like other online activity concepts. Nearly all schools and institutions today use eLearning in some capacity. The COVID-19 pandemic and the rapidly changing globe necessitate that everything is done online in addition to the world being entirely digital. This study employed the systematic literature review (SLR) methodology. The outcomes, which can be employed in a variety of Machine Learning (ML) applications, are acquired. Artificial intelligence (AI) in the form of machine learning (ML) enables software applications to predict outcomes more accurately even when they are not expressly programmed to do so. To forecast new output values, machine learning algorithms use historical data as input.
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