MACHINE LEARNING MODELS OF INFORMATION RECOMMENDATION SYSTEM ON INDIVIDUALIZATION OF EDUCATION
Author(s): Taratukhina Yulia Viktorovna, Vlasov Vladimir Vladimirovich, Bart Tatiana Vyacheslavovna
Rubric: Educational environment
Release: 2019-2 (27)
Keywords: individualization of education, machine learning models, big data analysis, recommendation systems
Annotation: Training model information recommendation system is associated with the study of applied mathematical and information methods and models, their combinations in order to ensure the necessary accuracy of the forecasts and conclusions. The article deals machine learning of model recommendation system using statistical methods and analysis of big data, aimed at addressing the issues of individualization of education. In this case, the accuracy of the machine learning model depends on the type of statistical model used to predict the probability of some event from the values of the set of features, as well as the training sample used to select the parameters, and the regularization function used to improve the generalizing ability of the resulting model. The study tested models based on logistic regression, methods of naive Bayesian classifier (Naïve Bayes), lasso-type regression. Experimentally confirmed the theoretical assumption about the possibility of creating a recommendation system on the individualization of education on the basis of an array of educational data, including the results of educational and extracurricular activities of students. Conclusions about the presence of correlation dependencies in the data, which can be used to improve the accuracy of the model of the recommendation system, are formulated.
Bibliography: Taratukhina YU.VI., Vlasov VL.VL., Bart TA.VY. MACHINE LEARNING MODELS OF INFORMATION RECOMMENDATION SYSTEM ON INDIVIDUALIZATION OF EDUCATION // Education Resources and Technologies. – 2019. – № 2 (27). – С. 7-14. doi: 10.21777/2500-2112-2019-2-7-14