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ACM.Приложение Д School segregation in the digital space Smirnov, I. International Conference on Computational Social Science 2017. The Internet provides students with a unique opportunity to connect and maintain social ties with peers from other schools no matter how far they are from each other. However, little is known about the real structure of such online relationships. In this paper, we investigate the structure of interschool friendship on a popular social networking site. We use data about 37,000 students from 590 schools of a large European city. We find that the probability of a friendship tie between close schools is high and that it decreases with distance following power law. We also find that students are more likely to be connected if educational outcomes of their schools are similar. We show that this fact is not a consequence of residential segregation. While high- and low-performing schools are evenly distributed across the city, this is not the case for digital space where schools turn out to be segregated by educational outcomes. There is no significant correlation between educational outcomes of a school and its geographical neighbours, however there is a strong correlation between educational outcomes of a school and its digital neighbours. These results challenge common assumption that the Internet is a borderless space and might have important implications for understanding of educational inequality in the digital age. School segregation in the digital spaceKeywords: digital inequality, social networks, education, academic performanceResearch on inequality in education has traditionally focused on the spatially localized context ofschools.
The spread of information technologies means that today’s schools operate in the globaldigital context too. We investigate the relationship between educational outcomes of schools andtheir digital context. We analyze a unique data set that contains information about one cohort ofhigh schools students (N = 36,951) from all schools of a large European city registered on apopular social networking site. The aim of the study is to estimate the extent of schoolsegregation in the digital space and to compare it with the geographical segregation.MethodsWe use data about one cohort of high school students from Saint-Petersburg, Russia registeredon a popular social networking site VK (Facebook analogue), N = 36,951.
Using this data weconstruct a school network where a tie between two schools exists if there is at least onefriendship tie between students from these schools, see Fig. 1. We measure position of a schoolin the digital space as its eigenvector centrality in the constructed network.We also use open data about geographical coordinates of schools and their educational outcomesmeasured as an average score at the unified state examination (USE) for the last 5 years. Finally,the prices of 11,018 apartments from the largest Russian real estate web-site were used toestimate the relative affluence of school neighborhoods.ResultsWe find a strong correlation between educational outcomes of a school and its position in thedigital space, r = 0.544 (p-value < 10-45), see Fig.
2a. This correlation is partly explained by thetotal number of students from that school registered on VK. However, the number of studentsexplains only 15% of outcomes variation while school position in the network explains 30%.We show that these results cannot be explained by the geographical position of a school (itsdistance from the city center).
The correlation between educational outcomes and the distancefrom the city center is -0.071 (p-value = 0.08), see Fig. 2b.The digital distance between schools depends on the physical distance (Fig. 3). However thedigital space and the physical space are structured differently. While there is a correlationbetween educational outcomes of a school and its k closest digital neighbors (r = 0.512, p-value< 10-40 for k = 30, see Fig. 4a), there is no significant correlation between educational outcomesof a school and its k closest geographical neighbors (r = 0.001, p-value = 0.97 for k = 30, see Fig.4b).
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