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School differentiation and its consequences: educational choice

Priority areas of development: sociology
2016

Goal of research was to describe the mechanisms of emergence and maintaining school differentiation in Russian cities. Our special focus was on the parental school choice as the cause and the result of school differentiation.

Methodology: We apply mixed methodology, combining observation and interviews with surveys and analysis of quantitative data. We use such statistical methods as factor analysis, regression analysis (multiple, logistic, and hierarchical), elements of spatial modeling, and structural equation modeling.

Empirical base of research: The project is based on both quantitative and qualitative data collected by the Laboratory in 2011-2015, and the new data gathered in 2016. Data collected: 1) observation in 18 schools (52 observations); 2) interviews with the representatives of district education committees and with parents (15 interviews); 3) a survey of 112 students of a technical college (the 7th wave of a longitudinal study); 4) a survey of high school students from the Krasnoselsky district (1200 students of 40 schools); 5) a survey of parents of the primary school students (729 parents from 19 schools); 6) a survey on health and risk behavior in vocational schools and technical colleges (971 students).

Results of research: We developed and tested two new methods: 1) assessment of school popularity using geocoding and school catchment area spatial analysis; 2) new instrument for school climate evaluation.

Using methods of spatial analysis we described the patterns of the spatial organization of school differentiation in two districts of St.Petersburg.

As the result of the study, we described the systems of school signals, and analyzed how they are perceived by the parents of different social status. Analysis of school observations, interviews and parental surveys revealed four groups of signals that schools send to parents: expected amount of expenses; the level of parent engagement into child’s education; emphasis on either academic or extracurricular aspect of school life, presence of barriers to school admission. We found out that parents perceive the signals differently based on their education and socio-economic status. According to their perception parents send their children to schools of certain status. Parents make choice in the context of existing differentiation, which is enhanced by the differences in perceptions of the school signals.

School differentiation results in the inequality of students’ achievements and aspirations. Peer influence and use of educational resources from the Internet can partly mitigate the effect of the adverse circumstances.

The study of the factors affecting the choice of the education after the 9th grade shows that friends are one of the strongest moderators of student’s behavior at this stage. Friends' aspirations can outweigh the motives of social status reproduction –not only enhance the aspirations, but also lower them.  Students who choose vocational schools and technical colleges stem from the same environment; their attitudes are more similar to each other’s than to those who prefer the higher education.

The survey of high school students of Krasnoselsky district revealed that using Internet for education is a common practice, though not as popular as using Internet for such recreation goals as social networking. Our analysis didn’t show any differences in Internet use practices among students of diverse socio-economic backgrounds and parental education levels. In other words, there is no digital divide of the second level. It remains to be confirmed whether this result holds on other samples of city students.

We studied the influence of different educational tracks' environment (school vs. technical college) on the risk behavior of the students. We described similarities and differences in risk practices (smocking and alcohol consumption) of the students of technical colleges and high schools, and identified factors affecting this behavior. Trusting family relationship and parental control appear to be protective factors. Peers drinking alcohol, as well as perception of harmlessness of the alcohol provoke adolescents to experiment with risk behavior.

Level of implementation,  recommendations on implementation or outcomes of the implementation of the results

Results of the study of school choice and risk behavior can be of interest for educational institutions (schools and technical colleges) as well as for educational committees. In the future, these results will help to develop recommendations for social policy in order to decrease inequality between schools and to prevent risk behavior.

The method of school popularity analysis based on the analysis of catchment areas and geocoding, along with the method of school climate evaluation, have been tested in two districts of Saint Petersburg (Nevsky and Krasnoselsky). The methods are ready to be applied in an on-line survey in schools of other districts on local committees’ request.

Laboratory prepares individual reports for educational institutions participating in our surveys, providing them with information on socio-psychological characteristics of their students, their academic attitudes, professional engagement and risk behavior (on aggregated levels of the entire educational institution and certain groups of students). These reports are useful for independent quality asessment.

Publications:


Poder K., Lauri T., Ivaniushina V., Alexandrov D. Family Background and School Choice in Cities of Russia and Estonia: Selective Agenda of the Soviet Past and Present // Studies of Transition States and Societies. 2016. Vol. 8. No. 3. P. 5-28. 
Иванюшина В. А., Александров Д. А., Казарцева Е. В. Этническая самоидентификация подростков-мигрантов // Журнал социологии и социальной антропологии. 2016. Т. XIX. № 2 (85). C. 113-127. 
Савельева С. С., Александров Д. А. Школьная дифференциация в социальном контексте: сравнительный анализ двух сельских кейсов // Социология образования. 2016. № 5. C. 85-97. 
Daniel A., Viktor K., Ilya M. Educational Migration from Russia to China: Social Network Data, in: WebSci '16: Proceedings of the 8th ACM Conference on Web Science. New York : ACM, 2016. С. 309-311. 
Daniel A., Alexey G., Ilya M. Virtual Caucasus on VK Social Networking Site, in: WebSci '16: Proceedings of the 8th ACM Conference on Web Science. New York : ACM, 2016. С. 215-217. 
Vadim V., Ilya M., Daniel A. Private and Public Online Groups in Apartment Buildings of St. Petersburg, in: WebSci '16: Proceedings of the 8th ACM Conference on Web Science. New York : ACM, 2016. С. 301-306. 
Савельева С. С., Александров Д. А. Локальные эффекты в воспроизводстве неравенства через систему образования, in: XVI Апрельская международная научная конференция по проблемам развития экономики и общества: в 4 кн.. Москва : Издательский дом НИУ ВШЭ, 2016. С. 634-641. 
Титкова В. В., Александров Д. А., Иванюшина В. А. Четыре стороны социальной агрессии: агрессоры, драчуны, жертвы и свидетели, in: XVI Апрельская международная научная конференция по проблемам развития экономики и общества: в 4 кн.. Москва : Издательский дом НИУ ВШЭ, 2016. С. 590-601. 
Khodorenko D. K., Titkova V. Extracurricular Sport and Risk Behaviour: Are They Related? / Высшая школа экономики. Series EDU "Education". 2016. No. 38.