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’.

Anomalous low-field magnetoresistance in $p$-type strained quantum wells is studied. It is experimentally shown that the Bychkov-Rashba mechanism leads to the cubic in quasimomentum spin-orbit splitting of the hole energy spectrum and the antilocalization behavior of low-field magnetoresistance is well described by the Hikami-Larkin-Nagaoka expression.

The results of an experimental study of interaction quantum correction to the conductivity of two-dimensional electron gas in $A_3{B}_5$ semiconductor quantum well heterostructures are presented for a wide range of $T\tau$ parameter $(T\tau \simeq 0.03-0.8)$, where $\tau$ is the transport relaxation time. A comprehensive analysis of the magnetic field and temperature dependences of the resistivity and the conductivity tensor components shows that the interaction correction can be divided into the ballistic and diffusion parts. It is shown that the ballistic part renormalizes in the main the electron mobility, whereas the diffusion part contributes to the diagonal and does not contribute to the off-diagonal component of the conductivity tensor. We have experimentally found the values of the Fermi-liquid parameters describing the electron-electron contribution to the transport coefficients, which are found to be in good agreement with the theoretical results.

Statistics of closed paths in two-dimensional (2D) systems, which just determines the interference quantum correction to conductivity and anomalous magnetoconductance, has been studied by computer simulation of a particle motion over the plane with randomly distributed scatterers. Both ballistic and diffusion regimes have been considered. The results of simulation have been analyzed in the framework of diffusion approximation. They are used for calculation of the magnetic-field dependence of magnetoconductance in the model 2D system. It is shown that the anomalous magnetoconductance can be, in principle, described by the well-known expression, obtained in the diffusion approximation, but with the prefactor less than unity and phase breaking, which differs from true value.

Learning analytics in MOOCs can be used to predict learner performance, which is critical as higher education is moving towards adaptive learning. Interdisciplinary methods used in the article allow for interpreting empirical qualitative data on performance in specific types of course assignments to predict learner performance and improve the quality of MOOCs. Learning analytics results make it possible to take the most from the data regarding the ways learners engage with information and their level of skills at entry. The article presents the results of applying the proposed learning analytics algorithm to analyze learner performance in specific MOOCs developed by Ural Federal University and offered through the National Open Education Platform.