ITM Web Conf.
Volume 35, 2020International Forum “IT-Technologies for Engineering Education: New Trends and Implementing Experience” (ITEE-2019)
|Number of page(s)||9|
|Section||Legislative and Regulatory Regulation of Engineering Digital Education|
|Published online||09 December 2020|
Determining the Smart University Infrastructure Development Level Based on Data Models
Togliatti State University, Belorusskaya str., 14, 445020, Togliatti, Russia
2 Ural State Pedagogical University, prosp. Kosmonavtov, 26, 620017, Yekaterinburg, Russia
3 Bauman Moscow State Technical University, 2nd Baumanskaya str., 5/1, 105005, Moscow, Russia
* Corresponding author: email@example.com
The paper presents applied research in the framework of solving determining the smart university infrastructure development level problem. Today, all digital transformation and transition to the digital economy concept and smart society development issues are very important. Education is the intellectual core of the digital transformation, the knowledge base, and the training center for the digital economy. Therefore, more attention should be paid to the creation and development of the smart university infrastructure. The purpose of the study is to develop criteria and models for determining the level of smart university infrastructure development. To describe the management of infrastructure level assessment, we propose to use adapted mathematical methods, structural analysis, and mathematical statistics methods. The study examines trends in the development of smart education, highlights the main elements of the smart university infrastructure (technological and organizational), and examines approaches to determining the level of its development based on data models. Taking into account the diversity and volume of quantitative and qualitative indicators of the smart university infrastructure, we propose to measure the level of the infrastructure using data models and Educational Data Mining tools. Universities can use got models when forming a development strategy or selecting funding directions, to reduce the time of digital transformation and transition to the optimal state of a smart university, to prepare for accreditation or to align the smart infrastructure of individual departments.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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