6 research outputs found
Schools are segregated by educational outcomes in the digital space
The Internet provides students with a unique opportunity to connect and
maintain social ties with peers from other schools, irrespective of 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 from
36,951 students from 590 schools of a large European city. We find that the
probability of a friendship tie between students from neighboring schools is
high and that it decreases with the distance between schools following the
power law. We also find that students are more likely to be connected if the
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 the
digital space, where schools turn out to be segregated by educational outcomes.
There is no significant correlation between the educational outcomes of a
school and its geographical neighbors; however, there is a strong correlation
between the educational outcomes of a school and its digital neighbors. These
results challenge the common assumption that the Internet is a borderless
space, and may have important implications for the understanding of educational
inequality in the digital age
Predicting PISA Scores from Students’ Digital Traces
The Programme for International Student Assessment (PISA) is an influential worldwide study that tests the skills and knowledge in mathematics, reading, and science of 15-year-old students. In this paper, we show that PISA scores of individual students can be predicted from their digital traces. We use data from the nationwide Russian panel study that tracks 4,400 participants of PISA and includes information about their activity on a popular social networking site. We build a simple model that predicts PISA scores based on students' subscriptions to various public pages on the social network. The resulting model can successfully discriminate between low- and high-performing students (AUC = 0.9). We find that top-performing students are interested in pages related to science and art, while pages preferred by low-performing students typically concern humor and horoscopes. The difference in academic performance between subscribers to such public pages could be equivalent to several years of formal schooling, indicating the presence of a strong digital divide. The ability to predict academic outcomes of students from their digital traces might unlock the potential of social media data for large-scale education research
The Palgrave Handbook of Digital Russia Studies
This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today
The Palgrave Handbook of Digital Russia Studies
This open access handbook presents a multidisciplinary and multifaceted perspective on how the ‘digital’ is simultaneously changing Russia and the research methods scholars use to study Russia. It provides a critical update on how Russian society, politics, economy, and culture are reconfigured in the context of ubiquitous connectivity and accounts for the political and societal responses to digitalization. In addition, it answers practical and methodological questions in handling Russian data and a wide array of digital methods. The volume makes a timely intervention in our understanding of the changing field of Russian Studies and is an essential guide for scholars, advanced undergraduate and graduate students studying Russia today