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With the propagation of E-learning platforms, monitoring and evaluating student performance and providing targeted content to enhance student learning capabilities is a major concern. This review will mainly focus on student behavior extraction and content formatting in relation to E-learning platforms. The usage of different data mining techniques to extract user interactions with E-learning platforms and identify patterns of interest have also been reviewed. The targeted recommendation of content can be done to each individual student. The review will be open to the path for manipulating content such that the interest of the target individuals remains high, due to the use of content translations and formatting across multiple media. There are some highlights of both student behavior extraction and content formatting. In student behavior extraction, person-alized learning, increased satisfaction, and early intervention could be achieved. The content formatting of an E-learning management system should be added headings to indicate the main topics of the paragraph, break up the text into shorter paragraphs to make it easier to read, add images to illustrate key concepts, and use a consistent style throughout the paragraph. Finally, these extracted concepts can be formed into a model and then we present a model taking into account student behavior and content formatting aspects. This model is formed based on the review. The model can be used to deal with intelligent E-learning systems where the system will be able to identify the student behaviors and then accordingly the content formatting.
Udugahapattuwa et al. (Thu,) studied this question.
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