Autors: Hrischev, R. H.
Title: Effective approach experience for practical training using web-based platforms and tools in the context of the COVID-19 pandemic
Keywords: e-learning, information systems, ERP systems, web-based desi

Abstract: This article is a result of the need to quickly and efficiently organize distance learning for students during the pandemic caused by COVID-19. Some e-learning opportunities provided by internet technologies, web-based platforms, online engineering tools are presented. Examples of training students in business information systems based on learning platforms and demo systems are presented, as well as the use of engineering tools for the design of production equipment on the websites of specialized companies. The common in these examples is the possibility of effective training without long preparation and additional expenses for the development of specialized learning platforms and simulators. The practical results of this non-standard approach to e-learning during a pandemic are also discussed.

References

    Issue

    Journal of Engineering Research and Sciences, issue 1, pp. 22-27, 2022, United States, JENRS, DOI: 10.55708/js0104003

    Full text of the publication

    Цитирания (Citation/s):
    1. Zainab Nadhim, Jawad Balázs, Machine learning-driven optimization of enterprise resource planning (ERP) systems: a comprehensive review, January 2024, Beni-Suef University Journal of Basic and Applied Sciences 13(1) DOI: 10.1186/s43088-023-00460-y - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    2. Chee Hong Koh, Bridging Web 4.0 and Education 4.0 For Next Generation User Training in ERP Adoption, Journal of Theoretical and Applied Information Technology 30th November 2023. Vol.101. No 22, ISSN: 1992-8645 - 2024 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science
    3. Inmaculada Tomeo-Reyes, Zhuoyu Chen, Investigating Different Approaches on Online Laboratory Practices, 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), DOI: 10.1109/TALE56641.2023.10398338 - 2024 - в издания, индексирани в Scopus или Web of Science

    Вид: статия в списание, публикация в реферирано издание, индексирана в Google Scholar