Analysis of Students Educational Interests Using Social Networks Data
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The paper presents an approach to analyze the structure of students educational interests based on data from social networks (subscriptions to pages and groups in the popular Russian social network Vkontakte). We collected data for 1379 students of Ural Federal University, who study at three institutes of the university. The students were clustered based on their interests in the social network and the clusters were compared with the institutes where students study. The approach allowed us to successfully separate the students who are interested in Computer Science and Humanitarian and Social Science. However, the students who study Economics and Management were not clustered successfully due to the heterogeneity of their interests. The approach could be used not only to determine the educational interests of existing students but also to recommend the most suitable educational area for prospective students based on social networks data.
KeywordsSocial networks analysis Machine learning Students interests Clustering Education
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