About this paper

Appears in:
Pages: 6077-6082
Publication year: 2017
ISBN: 978-84-697-3777-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2017.2375

Conference name: 9th International Conference on Education and New Learning Technologies
Dates: 3-5 July, 2017
Location: Barcelona, Spain


A. Feshchenko, V. Goiko, A. Stepanenko

National research Tomsk state university (RUSSIAN FEDERATION)
Social media is an important element of today's university communication policy. They deliver information directly to the target audience with no middlemen and guarantee vast reach with moderate financial investments. Meanwhile existing targeting methods of advertisement do not let universities define personal learning needs and interests of potential entrants and provide them with individual recommendations on choosing educational programs. That is the reason why universities create universal communities in social media and promote all the educational programs at the same time in the framework of their recruitment campaigns. This approach does not imply differentiation of the target audience according to their interests and attract their attention to educational programs that meet them.

Current methods of analyzing user’s data in social networks make university recruitment campaigns more effective. Our paper covers Tomsk State University experience in applying big data analysis methods for selecting promising entrants from social networks. We are going to share our success in these entrants involvement and retention in university informational space. The study is based on content analysis and statistical methods, interview and data mining.

The method of automated analysis designed for working with texts from social networks accounts lets us discover entrants interest in any subject area (history, mathematics, biology, etc.). Analyzing the topics of virtual communities the entrants follow, we clarify the information about their interests and evaluate their motivation to education and entering a university. Comparative data analysis of a significant number of possible entrants (100 - 500 thousand people) provides us with a list of highly-motivated learners interested in some subject (10 - 20 thousand people) and enables us to establish relationships with them. Specially designed communities in social networks for 4 specialties (Humanities, Natural Science, Physics and Mathematics and Economics) provide entrants with a comprehensive information about those programs which meet their interests.

A suggested approach allows to enhance the effectiveness of university marketing communications in social media, relevance of educational programs to users interests and entrants satisfaction with the university in the future, educational level of the entrants and their success in studying after entering the university.
author = {Feshchenko, A. and Goiko, V. and Stepanenko, A.},
series = {9th International Conference on Education and New Learning Technologies},
booktitle = {EDULEARN17 Proceedings},
isbn = {978-84-697-3777-4},
issn = {2340-1117},
doi = {10.21125/edulearn.2017.2375},
url = {http://dx.doi.org/10.21125/edulearn.2017.2375},
publisher = {IATED},
location = {Barcelona, Spain},
month = {3-5 July, 2017},
year = {2017},
pages = {6077-6082}}
AU - A. Feshchenko AU - V. Goiko AU - A. Stepanenko
SN - 978-84-697-3777-4/2340-1117
DO - 10.21125/edulearn.2017.2375
PY - 2017
Y1 - 3-5 July, 2017
CI - Barcelona, Spain
JO - 9th International Conference on Education and New Learning Technologies
JA - EDULEARN17 Proceedings
SP - 6077
EP - 6082
ER -
A. Feshchenko, V. Goiko, A. Stepanenko (2017) RECRUITING UNIVERSITY ENTRANTS VIA SOCIAL NETWORKS, EDULEARN17 Proceedings, pp. 6077-6082.