This article aims to highlight various methods and approaches to grouping countries, according to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolating all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more extensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrelationships between countries.

Subject. The study focuses on the impact of the digital economy determinants of the education transformation. Objectives. The article provides our own approach treating the education capital as a specific asset of the digital economy, which has an acceleration effect and sets up new trends in education through integrative networks. Methods. The study is based on principles of the systems integration, cross-disciplinary and multidisciplinary approaches. Results. The socio-economic progress was found to be determined with properties of human capital, which are solely specific to the digital economy. In new circumstances, it gets more important for actors of global, national, corporate and social networks to more actively cooperate within distributed networks in order to train high professionals, who would have skills in information networks. Thus, they would raise a new form of human capital – the capital of network education (network-based education capital). We describe positive externalities that arise when the educational sector joins communication processes. We illustrate how educational forms evolves, which are typical of a certain phase of the socio-economic development. The education capital was discovered to grow into a specific asset generating the quasi-rent and working as a social ladder only provided more actors are involved into the network. Conclusions and Relevance. Studying the evolution of educational forms through the cross-disciplinary method, we discovered the need for a system approach, which would help substantiate its transformation in the time of the digital economy, and the emergence of network-based education. These are technologies and tools of the digital economy that become unique factors generating the acceleration effect of the educational capital and ensuring the use of diverse network effects for the formation of intellectual capital and their social transformation.