*Five Years Citation in Google scholar (2016 - 2020) is. 1451*   *    IJPR IS INDEXED IN ELSEVIER EMBASE & EBSCO *       

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INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

A Step Towards Excellence
Published by : Advanced Scientific Research
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0975-2366
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IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

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The Prediction of Educational Success of StudentsHumanitarians in Social Networks from the Psychometric View

Author: FAIL GAFAROV, ZULFIRA ENIKEEVA, GALIM VAKHITOV, KONSTANTIN NIKOLAEV
Abstract: The work is one of the pilot studies within the framework of the development of a theoretical and applied model for predicting a person’s life activity in its educational activities through social networks. This study revealed that social networks are the result of profound changes in social reality models due to the intensive “digitalization” of modern society. In general, social networks reflect the virtualization of social processes which include the fusion of social and virtual realities. The behaviour of a person within the framework of social networks is reflected in the products of its virtual activity - the quantitative and qualitative (meaningful) metrics of its personal profile (friends, posts, likes, subscribers, etc.). As part of the hypothesis that the similarity of social networks users’ virtual behaviour indicates the similarity of their real behaviour, we assume that a number of personal profile characteristics are psychometric predictors of future students' academic achievements. The interval distribution regularities of average quantitative characteristics values for high-achieving and lowachieving humanists’ personal profiles in social networks (number of friends, communities, subscribers and photos) are shown based on their comparative analysis. As a result, psychometric predictors for academic achievements of humanists, which allow predicting their academic performance in the educational process, are presented in the work as a first approximation.
Keyword: academic achievements, psychometry, predictors, virtual space, social networks, prediction.
DOI: https://doi.org/10.31838/ijpr/2020.12.01.155
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