Innovative methodology and technology
Kashpur V.V., Gubanov A.V., Feshchenko A.V., Izofatova M.S., Kobenko A.V. —
Correlation between academic achievements of high school students and their digital shadow in social network
// Pedagogy and education.
– 2020. – ¹ 4.
– P. 37 - 51.
DOI: 10.7256/2454-0676.2020.4.33952 URL: https://en. nbpublish.com/library_read_article.php?id=33952
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This article examines the correlation between digital shadow of high school students in the social network “Vkontakte” and their high academic achievements (medal places in All-Russian Olympiads). Using the API “Vkontakte”, the author pulled digital shadow of the users, classifies whether a student is a medalist of Olympiads, and which groups of variables (personal characteristics, popularity, recentness of posts, and subscriptions to communities) have strongest influence upon the classification accuracy. User data from “Vkontakte” social network of 12,588 graduates of 2019 and 2020, and algorithms for machine learning are used for classification. As a result the conducted research, correlation is established between the level of educational potential, as a student's ability to win in Olympiads, and the content appealing to such students. The author also outlines 63 communities in “Vkontakte” that are most significant for carrying out classification of students participating in the research. Among the communities that enjoy most popularity among the winners of Olympiads, are those related to science and passing the unified state exam, as well as intellectual memes. The subscriptions of students not participating in the Olympiads indicate the communities featuring humor and entertainment content. This observation contradicts the opinion widespread in pedagogical community on the negative impact of social networks upon academic achievements of the students. The online platform “Vkontakte” contains not only entertainment content, but also information that stimulates cognitive interest and academic motivation.
random forest, Vkontakte, digital footprint, machine learning, social networks, educational achievements, educational olympiad, community, Jupyter, big data
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