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Research in the fields of educational data mining (EDM) and learning analytics (LA) has become more intriguing since it reveals practical information from educational databases for a variety of uses, including forecasting students’ progress. Predicting a student’s performance can be useful for decisions in contemporary educational systems. While family expenditures and student personal information are frequently disregarded, existing techniques have employed aspects that are mostly connected to academic success and family financial assets. Learning analytics, discriminative classification models, and generative classification models are used to forecast a student’s likelihood of successfully completing his degree.
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