The increasing amount of educational data has rapidly in the latest few years. The Educational Data Mining (EDM) techniques are utilized to detect the valuable pattern so that improves the educational process and to obtain high performance of all educational elements. The proposed work contains three stages: preprocessing, features selection, and an active classification stage. The dataset was collected using EDM that had a lack in the label data, it contained 2050 records collected by using questionnaires and by using the students’ academic records. There are twenty-five features that were combined from the following five factors: (curriculum, teacher, student, the environment of education, and the family). Active learning had been utilized in the classification. Four techniques had been applied for classifying the features: Random Forest (RF) algorithm, Label Propagation (LP), Logistic Regression (LR), and Multilayer Perceptron (MLP). The accuracies of prediction were 95.121%, 92.195%, 92.292%, and 93.951% respectively. Also, the RF algorithm has been utilized for assorting the features depending on their importance.
The author addresses the issue of the linguoculturological component in the process of teaching Russian to Arabic students, focuses on the peculiarities of the national character of students. The author also refers to the long-standing ties of Russian and Arab cultures, thus emphasizing the relevance of this aspect for the current state and situation of the Russian language in Arab countries.
Автор статьи обращается к вопросу лингвокультурологической составляющей в процессе преподавания русского языка арабским студентам, останавливается на особенностях национального хара
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