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Learning analytics to predict student achievement in online learning during Covid-19 mitigation

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Description:
This study aims to make predictions of online learning during the COVID-19 mitigation period by using Analytic Learning Techniques. Learning is done using Moodle as the learning management system. The primary statistical technique used in this study is cluster analysis, which groups students in three different characteristics based on the activity components. The results of the study indicate that the activity components that support social presence are the determining components in predicting learning success. Another consequence is that the three clusters formed can be identified as high, medium, and low groups in progress with the identifier of activity components. Chatting, Forum, Choice, and Assignment are the practical activity components in this finding. The result of this study is too early to state that the e-learning is successful during COVID-19 mitigation. More information is still needed for further analysis. (author abstract)
Resource Type:
Reports & Papers
Country:
Indonesia

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