To respond to the needs of digital transformation, universities must continue to play their role as proving ground for educating the future generation and innovation. The article is devoted to overview, discussion, and investigation of application in higher education of two modern information technologies: big data and internet of things. The article identifies the role of analytics, based on big data, in improvement of education process and outlines the challenges, related with big data mining, storage, and security. Proposed statements are based on practical experience of the authors; architecture of program and methodological solution are the focus of the article. The article contributes to theory by the new approach to combination of big data and internet of things technologies in educational resources and, at the same time, includes implications for practice, presenting examples of the approach's realization and sharing the authors' experiences of such realization.

Web-based learning has been developed by the majority of academic institutions and organizations worldwide due to its obvious benefits for both educators and learners. Meanwhile, many of the existing developmental approaches in this sphere lack one crucial consideration necessary for implementing web-based learning at academic institutions. In this article, the authors identify two processes: development of distance learning and digitalization of training that are represented now at almost every academic university. The article singles out the stages of their development and shows that the processes lead to the same result - a new quality of education. The authors focus on figuring out which path is more effective for achieving the highest level of development depending on university's characteristics. Accumulation of detailed information in the Electronic informational educational environment about current learning outcomes creates opportunities for emerging trends, such as Learning Analytics, Personalized Learning and Adaptive Learning. In data analysis using BigData and Machine Learning technologies.

In the article the avatar-based learning and teaching (A-BL&T) as a concept of control and managing knowledge in modern socio-economic conditions is proposed to use for assessment a university's economic efficiency. It is shown that all elements, methods and techniques (tools) do not operate in isolation, but rather are interrelated, complementing each other. All of them are used in the process of management and, in a combination, are powerful tools for increasing efficiency of management. Based on the example of avatar-based learning and teaching in Russian universities as modern educational environments, a conceptual model, methodology, and methods have been obtained for the automation of planning and calculation of the academic load in the university.

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In this paper, a combination of neural network with sliding mode control (SMC) is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In such an approach two parallel Neural Networks (NNs) are proposed to realize the SMC. The equivalent control and the corrective control term of SMC are the outputs of the NNs. The training algorithms applied to NNs are based on the SMC equation with using a gradient descent method to minimize the control and chattering while optimize the error performance. In this paper, a Neuro-Sliding Mode Controller in Power Systems is proposed and experimental results are presented. Two parallel NNs are used to realize the neuro-SMC. To increase the first neural network structure flexibility hidden layer neuron pruning and node splitting algorithms are considered.