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Since the main platform of online education, MOOCs give learners with wealthy course sources, but class designers are still confronted with the problem of the way to accurately boost the standard of programs. Current study generally concentrates on learners’ emotional responses on distinctive program attributes, neglecting non-psychological information and also the costs needed to strengthen these attributes. This limitation causes it to be complicated for program designers to fully grasp the real wants of learners and also to properly Track down The real key issues within the course. To beat the above difficulties, this research proposes an MOOC improvement approach based on textual content mining and multi-attribute decision-making. To start with, we employ phrase vectors and clustering approaches to extract system characteristics that learners target from their reviews. Secondly, Along with the help of some deep Finding out techniques depending on BERT, we perform a sentiment Assessment on these feedback to expose learners’ emotional tendencies and non-emotional written content in the direction of class characteristics.

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