Institute of Education

Maps and diagrams have long been used by science and education. The results and achievements of geography, astronomy, biology, economics have always been presented in the form of maps. Modern methods and tools of network science allow to deeper understand collaboration because relations between agents of activity are represented as a map. For many collaborative educational systems maps of relations between agents and activity products are built automatically. However, these diagrams are not used in educational practice as tools for better learning. The paper provides examples of how the diagrams were used in educational practice in order to support a group reflection of collaborative activities.

Digital traces have become an essential source of data in social sciences because they provide new insights into human behavior and allow studies to be conducted on a larger scale. One particular area of interest is the estimation of various users’ characteristics from their texts on social media. Although it has been established that basic categorical attributes could be effectively predicted from social media posts, the extent to which it applies to more complex continuous characteristics is less understood. In this research, we used data from a nationally representative panel of students to predict their educational outcomes measured by standardized tests from short texts on a popular Russian social networking site VK. We combined unsupervised learning of word embeddings on a large corpus of VK posts with a simple, supervised model trained on individual posts. The resulting model was able to distinguish between posts written by high- and low-performing students with an accuracy of 94%. We then applied the model to reproduce the ranking of 914 high schools from 3 cities and of the 100 largest universities in Russia. We also showed that the same model could predict academic performance from tweets as well as from VK posts. Finally, we explored predictors of high and low academic performance to obtain insights into the factors associated with different educational outcomes.

Digital traces are often used as a substitute for survey data. However, it
is unclear whether and how digital traces actually correspond to the survey-based
traits they purport to measure. This paper examines correlations between selfreports
and digital trace proxies of depression, anxiety, mood, social integration
and sleep among high school students. The study is based on a small but rich
multilayer data set (N = 144). The data set contains mood and sleep measures,
assessed daily over a 4-month period, along with survey measures at two points
in time and information about online activity from VK, the most popular social
networking site in Russia. Our analysis indicates that 1) the sentiments expressed
in social media posts are correlated with depression; namely, adolescents with
more severe symptoms of depression write more negative posts, 2) late-night
posting indicates less sleep and poorer sleep quality, and 3) students who were
nominated less often as somebody’s friend in the survey have fewer friends on VK
and their posts receive fewer “likes.” However, these correlations are generally
weak. These results demonstrate that digital traces can serve as useful supplements
to, rather than substitutes for, survey data in studies on adolescents’ well-being.
These estimates of correlations between survey and digital trace data could provide
useful guidelines for future research on the topic.

Numerical information can be represented in three formats: two symbolic (visual (digits) and verbal (number words)) and one nonsymbolic (analog) format. Studies have shown that the precision of symbolic numerical representation is associated with math performance. The precision of symbolic representation is mostly discussed as the precision of representation in a visual format, whereas the precision of representation in verbal format and its relation with math performance is less studied. The current study examines the precision of symbolic numerical representation in visual and verbal formats and the relationship between such precision and math performance when controlling for prior math performance, nonsymbolic numerical representation, phonological processing, reading skills and working memory. We used data from 367 Russian first graders (mean age, 7.6 years; 53% girls). To assess the precision of symbolic numerical representation, magnitude comparison tasks with digits and number words were used. It was found that the precision of symbolic representation in verbal format did not have a direct effect on math performance, but has an indirect effect via visual format of symbolic representation, even when controlling for prior math performance and other cognitive abilities.