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The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students.

, , , , , and . Machine Learning Paradigms, volume 158 of Intelligent Systems Reference Library, Springer, (2020)

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Combining ensembles algorithms of symbolic learners., and . IISA, page 1-6. IEEE, (2015)A note on parallel approximate pseudoinverse matrix techniques for solving linear least squares problems., , , and . J. Comput. Sci., (2020)Integrating global and local boosting., , , and . IISA, page 1-6. IEEE, (2015)An adaptive cluster-based sparse autoregressive model for large-scale multi-step traffic forecasting., , , , and . Expert Syst. Appl., (2021)Empirical investigation of factors influencing function as a service performance in different cloud/edge system setups., , , , and . Simul. Model. Pract. Theory, (November 2023)A hybrid Machine Learning methodology for imbalanced datasets., and . IISA, page 252-257. IEEE, (2014)Predicting Student Performance in Distance Higher Education Using Active Learning., , , and . EANN, volume 744 of Communications in Computer and Information Science, page 75-86. Springer, (2017)The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students., , , , , and . Machine Learning Paradigms, volume 158 of Intelligent Systems Reference Library, Springer, (2020)