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Microlearning in Forming the Students' English Competencies with VR Involvement
Author(s) -
Olha Fedorova,
Oleksandr Shumskyi,
Oksana Golikova,
Iryna Kutsenko,
Наталія Сердюк,
Oksana Zahorodna
Publication year - 2022
Publication title -
broad research in artificial intelligence neuroscience
Language(s) - English
Resource type - Journals
eISSN - 2067-3957
pISSN - 2068-0473
DOI - 10.18662/brain/13.1sup1/326
Subject(s) - process (computing) , modernization theory , context (archaeology) , computer science , virtual learning environment , knowledge management , mathematics education , psychology , multimedia , political science , paleontology , law , biology , operating system
Every year the problem of higher educational establishments’ modernization, introduction of the newest methods and strategies of educational process reorganization, estimation of achievements in the context of information technologies becomes more and more urgent. The situation with the COVID-2019 pandemic has demonstrated the need to involve additional information approaches to teaching within the system of blended learning in higher educational establishments. As a result, the question arises: what mechanisms can be used to make learning effective? The article reveals the concept of “microlearning” as the latest approach in education and identifies its key tools in formation of English-language competencies in university students. The study found that microlearning can be an effective complement to traditional learning, as it extends the learning process beyond the classroom on the basis of information technology, allows consolidating virtually and deepening the knowledge gained during practical classes, and provokes students to daily communication outside the establishment through social network. Moreover, we propose a strategy for implementation of microlearning on the example of the educational process at Kherson State Maritime Academy through interaction of practical classes in the line of communicative-competency approach, virtual and online learning on the principle of rotation.

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