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- W4385433718 abstract "Academic performance of the student in higher education are among of the important data for educators and employers in this new era. The issue to analyse a large number of data has always been a constraint to most employers and educators. Data mining techniques were seen as one of the suitable approaches to reduce the delay in the analysis and the generation of output. However, proper, and high accurate techniques that serve as best fitted models for student performance prediction have yet to be found Therefore, an effective automated student performance prediction mechanism for higher education is very much in demand. This paper reviews the data mining techniques that are specific for the student performance evaluation. Data mining techniques that are reviewed include the SVM, KNN, Naive Bayes and Decision Tree. These techniques are evaluated for accuracy and the best fitted models for the student performance are higlighted." @default.
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- W4385433718 date "2023-01-01" @default.
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- W4385433718 title "Data Mining Techniques for Student Performance Prediction: A Review" @default.
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- W4385433718 doi "https://doi.org/10.1007/978-3-031-28314-7_4" @default.
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