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There has been a rapid growth of massive open online courses (MOOCs) in the global education market in the last decade. Online learning technologies are becoming increasingly widespread in the non-formal education sector and in higher and supplementary vocational education. The use of MOOCs in Russia to support the delivery of educational programmes at university level opens opportunities in terms of expanding the educational choice for students, the development of virtual academic mobility, reduction in the cost of educational services, and improvement in the accessibility of education. However, the effectiveness of using different online learning technologies at university level, and the consequences of their widespread adoption, has not been sufficiently explored. In this research study, a comparative analysis is made of the effects of different online learning models on student educational outcomes in a university setting. A study was undertaken in which different groups of students at the Ural Federal University, Russia, were encouraged to study technical and humanities disciplines using a framework of blended learning, and online learning with tutoring support. The results of the study were compared with the results of a reference (control) group of students who studied the same disciplines in a traditionally taught model. It was found that both models (blended and online) of MOOC implementation demonstrated greater learning gains, in comparison with the traditional model. For engineering and technical disciplines, there was no statistically significant difference between blended or online learning technologies. For the humanities discipline, where the communicative component of the learning process was significant, the blended learning technology produced better results. Conclusions of this empirical research may be useful for heads of educational organizations and teachers in helping them to make strategic decisions about the modernization of university courses by increasing the effectiveness of the implementation of new educational technologies. The results of this research project will be used for implementing the State Priority Project, ‘The Modern Digital Educational Environment of the Russian Federation’. © 2018, © The Author(s) 2018.

The creation and implementation of the strategies for economic and social development in the Russian regions for the period up to 2035 implies an adequate development of transport services affecting all economic sectors and segments of the population. In this regard, we propose a model connecting the characteristics of passenger and cargo flows with the parameters of economic and social development, as well as with the region’s demography. This model allows specifying the congestion of the transport system resulting from the implementation of plans for social and economic development and planned decisions in the sphere of economic activity. For developing the model, we selected parameters describing the economic situation, labour market, demography, living standards and social situation in the analysed subject. These parameters have the highest correlation coefficients with the analysed characteristics of the transport infrastructure. Further, we conducted a step-by-step regression analysis, adding to the already existing variables new ones that gave the greatest increase in the determinacy coefficient R2. The model shows that the main factor determining the amount of passengers transported by public buses is the annual average number of employed persons. The passenger turnover is mostly affected by the population size. The volume of goods transported by trucks is determined by parameters characterising the level of the production development (investments in fixed assets, fixed capital in the economy, and the volume of shipped goods of domestic production). The use of nonlinear models and networks did not significantly reduce the model’s errors. Additionally, we clustered the Russian regions by indicators of socio-economic development and the characteristics of transport infrastructure affecting traffic flows. Then we assessed the efficiency of transport infrastructure’s exploitation in various clusters. This allows the targeted benchmarking, namely the selection of regions mostly appropriate for comparison with the analysed one. © 2019 Institute of Economics, Ural Branch of the Russian Academy of Sciences. All rights reserved.