Attraction and retention of applicants with high educational potential with the help of open user data of the social network "Vkontakte"
The paper presents the results of the project of Tomsk State University on the analysis of big data in social networks for searching and attracting talented students. The analysis of the open user data of applicants from the social network allows to predict the cognitive abilities of high-ranking juniors with high accuracy. The accuracy of predicting a high level of intelligence for boys was 69%, girls 57%; boys' creativity 83%, girls 78%; boys motivation 90%, girls 82%. The accuracy of forecasting interests in the humanities and social sciences was 81%, natural sciences 84%, and exact sciences 93%. In 2017, with the help of the developed analysis algorithms, 126,000 potential «Vonakte» profiles were analyzed by potential applicants. We enrolled 9,000 high school students with high potential in the humanities, 900 of them invited to Tomsk State University, 54 of them enrolled at TSU. Equivalent analysis for 2017-2018 control group of applicants identified using the developed model, with applicants admitted to TSU through the traditional recruiting system, showed higher quality indicators of the first group. Improving the forecasting model made it possible in 2018 to analyze 300 thousand profiles of potential applicants for undergraduate and TSU specialties, to identify high school students with a high level of signs of giftedness and level of educational motivation, to organize targeted work with them through the social network on vocational orientation and invitation to university educational programs. This approach allows you to complement the main criterion in the selection of applicants to the university, the Unified State Exam, and to increase the effectiveness of the university's recruiting activities for talented students. An analysis of the success of university studies of such students in the first year shows the promise of such an approach. At the same time, the developed methodology can be used to work with students inside the university. The development perspective of the research is connected with longitudinal observation of the educational trajectory and change of digital traces in social networks of students with high educational potential, forecasting their future professional potential based on these data, creating a personnel reserve for the region's economy to reduce the outflow of human capital, developing algorithms personal recommendations to university graduates on career development paths (long life learning).