3Summary (Дифференциация учащихся по академической успеваемости в социальной сети), страница 4

Further research might seekanswers to these important questions.Our results may have implications for the practice of education due to the important role thatSNSs play in the life of modern students. The main component of the SNS is a newsfeed that isconstructed from information posted by friends and from subscriptions to various public pages. Weshowed that both of these sources are differentiated by academic performance. It means thateveryday digital flow of information is fundamentally different for students with varying academicperformance. This fact should be taken into account by teachers in their everyday practice.

Thedigital environment of students is out of the control of traditional pedagogical tools; however,teachers could influence it. For instance, one of the main features of modern SNSs isrecommendation algorithms that suggest new information based on the history of users’ behavior.Our research showed that this might lead to information bubbles of horoscopes and cool gags forlow-performing students. Teachers, however, might ask their students to find some educationalinformation on SNSs or elsewhere on the Internet.

This student activity will lead to digital tracesthat later could be used by recommendation algorithms to show students new information thatmight be more enriching than information that would be shown without intervention. Developmentof similar pedagogical interventions along with studying of their potential impact may be a logicalcontinuation of our work.20REFERENCES1. Watts D.J.

A twenty-first century science // Nature. 2007. Vol. 445. №. 7127. P. 489–489.2. Lazer D., Pentland A., Adamic L., Aral S., Barabási A., Brewer D., Christakis N.,Contractor N., Fowler J., Gutmann M., Jebara T., King G., Macy M., Roy D., Alstyne M. V.Computational social science // Science. 2009. Vol. 323. №. 5915. P. 721–723.3. McFarland D. A., Lewis K., Goldberg A. Sociology in the era of big data: The ascent offorensic social science // The American Sociologist. 2016. Vol. 47. №. 1. P. 12–35.4. Kandel D. B. Homophily, selection, and socialization in adolescent friendships //American journal of Sociology.

1978. Vol. 84. №. 2. P. 427-436.5. Tuma N. B., Hallinan M. T. The effects of sex, race, and achievement on schoolchildren'sfriendships // Social Forces. 1979. Vol. 57. №. 4. P. 1265-1285.6. Shrum W., Cheek Jr. N. H., MacD S. Friendship in school: Gender and racial homophily// Sociology of Education. 1988. P. 227-239.7. Flashman J. Academic achievement and its impact on friend dynamics // Sociology ofeducation. 2012. Vol. 85. №. 1. P.

61-80.8. Currarini S., Jackson M. O., Pin P. Identifying the roles of race-based choice and chancein high school friendship network formation // Proceedings of the National Academy of Sciences.2010. Vol. 107. №. 11. P. 4857-4861.9. Lomi A., Snijders T. A., Steglich C. E., Torló V. J. Why are some more peer than others?Evidence from a longitudinal study of social networks and individual academic performance //Social Science Research. 2011. Vol. 40. №. 6. P.

1506-1520.10. Eagle N., Pentland A. S., Lazer D. Inferring friendship network structure by usingmobile phone data // Proceedings of the national academy of sciences. 2009. Vol. 106. №. 36.P. 15274-15278.11. Mastrandrea R., Fournet J., Barrat A. Contact patterns in a high school: a comparisonbetween data collected using wearable sensors, contact diaries and friendship surveys // PloS one,2015. Vol. 10.

№. 9, e0136497. URL: https://doi.org/10.1371/journal.pone.0136497 (датаобращения: 07.06.2018).12. Stephens-Davidowitz S. Everybody lies: Big data, new data, and what the internet cantell us about who we really are. – Dey Street Books, 2017.13. OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education,PISA,OECDPublishing,обращения: 07.06.2018).Paris,URL:https://doi.org/10.1787/9789264266490-en(дата2114. Bond R. M., Fariss C. J., Jones J. J., Kramer A. D., Marlow C., Settle J.

E., Fowler J. H.A 61-million-person experiment in social influence and political mobilization // Nature. 2012. Vol.489. №. 7415. P. 295–298.15. Kramer A. D., Guillory J. E., Hancock J. T. Experimental evidence of massive-scaleemotional contagion through social networks // Proceedings of the National Academy of Sciences.2014. Vol. 111. №. 24. P. 8788–8790.16. Hobbs W. R., Burke M., Christakis N. A., Fowler J. H.

Online social integration isassociated with reduced mortality risk // Proceedings of the National Academy of Sciences. 2016.Vol. 113. №. 46. P. 12980-12984.17. Preoţiuc-Pietro D., Volkova S., Lampos V., Bachrach Y., Aletras N. Studying userincome through language, behaviour and affect in social media // PloS one. 2015. Vol. 10. № 9,e0138717. URL: https://doi.org/10.1371/journal.pone.0138717 (дата обращения: 07.06.2018).18. An J., Weber I.

# greysanatomy vs.# yankees: Demographics and hashtag use on twitter.Proceedings of the Tenth International Conference on Web and Social Media. - Cologne, Germany:AAAI Press, 2016.19. Rao D., Paul M., Fink C., Yarowsky D., Oates T., Coppersmith G. Hierarchical bayesianmodels for latent attribute detection in social networks. In Proceedings of the InternationalConference on Weblogs and Social Media.

2011.20. Gebru T., Krause J., Wang Y., Chen D., Deng J., Aiden E. L., Fei-Fei L. Using deeplearning and Google Street View to estimate the demographic makeup of neighborhoods across theUnited States // Proceedings of the National Academy of Sciences. 2017. Vol. 114. №. 50.P. 13108–13113.21. Watts D. J. Common sense and sociological explanations // American Journal ofSociology. 2014. Vol. 120.

№. 2. P. 313-351.22. Hofman J. M., Sharma A., Watts D. J. Prediction and explanation in social systems //Science. 2017. Vol. 355. №. 6324. P. 486–488.23. Von Hayek F. A. The pretence of knowledge // The American Economic Review. 1989.Vol. 79. №. 6. P. 3–7.24. Kosinski M., Stillwell D., Graepel T. Private traits and attributes are predictable fromdigital records of human behavior // Proceedings of the National Academy of Sciences. 2013.Vol. 110. №. 15. P. 5802–5805.25.

Krasilnikov A., Semenova M. Do Social Networks Help to Improve Student AcademicPerformance? The Case of Vk.com and Russian Students // Economics Bulletin. 2014. Vol. 34.№. 2. P. 718–733.2226. Докука С. В., Валеева Д. Р., Юдкевич М. М. Коэволюция социальных сетей иакадемических достижений студентов // Вопросы образования. 2015. №3. С. 44–65.27. Alexandrov D., Karepin V., Musabirov I.

Educational migration from Russia to China:social network data. In Proceedings of the 8th ACM Conference on Web Science. 2016.URL: doi:10.1145/2908131.2908192 (дата обращения: 07.06.2018).28. Королева Д. О. Всегда онлайн: использование мобильных технологий исоциальных сетей современными подростками дома и в школе // Вопросы образования. 2016.№ 1.С.

205-224.29. Королева Д. О. Исследование повседневности современных подростков:присутствие в социальных сетях как неотъемлемая составляющая общения // Современнаязарубежная психология. 2016. Т. 5. № 2. С. 55-61.30. Google (2017). Новое поколение интернет-пользователей: исследование привычеки поведения российской молодежи онлайн. URL: https://www.thinkwithgoogle.com/intl/ruru/research-study/novoe-pokolenie-internet-polzovatelei-issledovanie-privychek-i-povedeniiarossiiskoi-molodezhi-onlain/ (дата обращения: 07.06.2018).31.Shareaholic.SocialMediaTrafficReport.2014.URL:https://blog.shareaholic.com/social-media-traffic-trends-01-2015/ (дата обращения: 07.06.2018).32.

Касамара В. А. Ценностные ориентация российского студенчества. 2017.URL: https://ioe.hse.ru/seminar1617 (дата обращения: 07.06.2018).33. Dimaggio P., Hargittai E., Celeste C., Shafer S. Digital inequality: From unequal accessto differentiated use. Social Inequality. – New York: Russell Sage Foundation, 2004. P. 355-400.34.

DiMaggio P., Hargittai E. "From the 'Digital Divide' to 'Digital Inequality': StudyingInternet Use as Penetration Increases" Working Papers 47. Princeton University, Woodrow WilsonSchool of Public and International Affairs, Center for Arts and Cultural Policy Studies. 2001. 4(1),4-2.35. DiMaggio P., Hargittai E., Neuman W. R., Robinson J. P. Social implications of theInternet // Annual review of sociology. 2001. Vol. 27. №. 1.

P. 307-336.36. Poole, G. A. A new gulf in American education, the digital divide. // New York Times.1996. 29.01.1996. D3.37. Hargittai E., Hinnant A. Digital inequality differences in young adults' use of the Internet// Communication Research. 2008. Vol. 35. №. 5. P. 602-621.38. Волченко О. В. Динамика цифрового неравенства в России // Мониторингобщественного мнения: экономические и социальные перемены. 2016.

№ 5. С. 163-18239. Hansen J. D., Reich J. Democratizing education? Examining access and usage patternsin massive open online courses // Science. 2015. Vol. 350. №. 6265. P. 1245-1248.2340. Kizilcec R. F., Davis G. M., Cohen G. L. Towards Equal Opportunities in MOOCs:Affirmation Reduces Gender & Social-Class Achievement Gaps in China.

In Proceedings of theFourth ACM Conference on Learning @ Scale. Cambridge, MA: 2017. P. 121-130.41. Redmiles E. M. Net Benefits: Digital Inequities in Social Capital, Privacy Preservation,and Digital Parenting Practices of U.S. Social Media Users.

In Proceedings of the TwelfthInternational AAAI Conference on Web and Social Media. 2018. P. 270-279.42. Бессуднов А.Р.,РоссийскоголонгитюдногоКуракин Д.Ю., Малик В.М., Янбарисова Д.М. Бюллетеньпанельного исследованияобразовательныхитрудовыхтраекторий. Выпуск 1. Национальная панель: первая волна (2011-12) // Научные докладыИнститута образования WP 02/IE/2014.43. Смирнов И. Б., Сивак Е. В., Козьмина Я. Я.

В поисках утраченных профилей:достоверность данных ВКонтакте и их значение для исследований образования // Вопросыобразования. 2016. №4. С. 106-122.44. Smirnov I., Thurner S. Formation of homophily in academic performance: Studentschange their friends rather than performance. // PloS one. Vol. 12. №. 8, e0183473.45. Поливанова К. Н., Смирнов И. Б. Что в профиле тебе моем: данные «ВКонтакте»как инструмент изучения интересов современных подростков // Вопросы образования. 2017.№2. C.

134-151.46. Smirnov I The Digital Flynn Effect: Complexity of Posts on Social Media Increases overTime. // SocInfo 2017. Lecture Notes in Computer Science. 2017. Vol. 10540. P. 24-30.47. Elizaveta Sivak, Ivan Smirnov. Gender Bias in Sharenting: Both Men and WomenMention Sons More Often Than Daughters on Social Media. 2018. arXiv:1803.10329.48. Кашарин, М. Ю.

Связь между участием студентов в студенческих организациях иихсоциальнымкапиталом.2017.Магистерскаяhttps://www.hse.ru/edu/vkr/206749166 (дата обращения: 07.06.2018).диссертация.URL:.