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Analysis of Students Educational Interests Using Social Networks Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11832)

Abstract

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.

Keywords

Social networks analysis Machine learning Students interests Clustering Education 

References

  1. 1.
    Boyd, D.M., Ellison, N.B.: Social network sites: definition, history, and scholarship. J. Comput.-Mediated Commun. 13(1), 210–230 (2008)CrossRefGoogle Scholar
  2. 2.
    Steinfield, C., Ellison, N.B., Lampe, C.: Social capital, self-esteem, and use of online social network sites: a longitudinal analysis. J. Appl. Dev. Psychol. 29(6), 434–445 (2008)CrossRefGoogle Scholar
  3. 3.
    Madge, C., Meek, J., Wellens, J., Hooley, T.: Facebook, social integration and informal learning at university: ‘it is more for socialising and talking to friends about work than for actually doing work’. Learn. Media Technol. 34(2), 141–155 (2009)CrossRefGoogle Scholar
  4. 4.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. National Acad. Sci. 110(15), 5802–5805 (2013)CrossRefGoogle Scholar
  5. 5.
    Landers, R.N., Schmidt, G.B.: Social media in employee selection and recruitment: an overview. In: Landers, R.N., Schmidt, G.B. (eds.) Social Media in Employee Selection and Recruitment, pp. 3–11. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-29989-1_1CrossRefGoogle Scholar
  6. 6.
    McDonald, P., Thompson, P., O’Connor, P.: Profiling employees online: shifting public-private boundaries in organisational life. Hum. Resour. Manag. J. 26(4), 541–556 (2016)CrossRefGoogle Scholar
  7. 7.
    Smirnov, I.: Predicting PISA Scores from Students’ Digital Traces. In: Proceedings Of The Twelfth International Conference On Web And Social Media. American Association for Artificial Intelligence (AAAI) Press, pp. 360–364 (2018)Google Scholar
  8. 8.
    Polivanova, K., Smirnov, I.: What’s in My Profile: VKontakte data as a tool for studying the interests of modern teenagers. Educ. Stud. 2, 134–152 (2017)Google Scholar
  9. 9.
    Koroleva, D.O.: The use of social networks in education and socialization of adolescents: an analytical review of empirical studies (international experience). Psychol. Sci. Educ. 20(1), 28–37 (2015).  https://doi.org/10.17759/pse.2015200104CrossRefGoogle Scholar
  10. 10.
    Pedregosa, F., Varoquaux, G., Gramfort, A., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Borisov, V.I., Rabovskaya, M.Y., Syskov, A.M., Zeyde K.M.: Design an information system for student track prediction. In: 2018 Siberian Symposium on Data Science & Engineering (SSDSE), Novosibirsk, pp. 24–27 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ural Federal UniversityEkaterinburgRussia
  2. 2.Krasovskii Institute of Mathematics and MechanicsEkaterinburgRussia

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