CLASSIFICATION OF NEURAL NETWORKS FOR CREATING EDUCATIONAL CONTENT BY UNIVERSITY EDUCATORS

Abstract

Neural networks today are one of the drivers of scientific, technological, and social development, which can change the image of modern higher education. The results of university educators’ survey have shown that one of the main directions of neural networks application in the professional activities of academic staff is the creation of teaching materials, namely, tests, lesson plans, exercises. Thus, neural networks enable to simplify the routine of class preparation.The research aims to develop the classification of neural networks that can be used to create educational content by university educators. The necessity to develop the classification is due to the constantly growing number of neural networks, their updating and development, which makes it difficult to choose the most appropriate type of network for content creation. To achieve the purpose, the authors analysed the relevant literature; developed the questionnaire aimed at analyzing the use of neural networks in the professional activity of university educators; classified certain categories of neural networks; described the functional capabilities of neural networks for creating educational content. As a result, three categories of neural networks were identified: interdisciplinary, specialized, and additional. Interdisciplinary neural networks include neural networks for generating texts, images, presentations, audio, video, online courses. Specialized neural networks include multitasking neural networks, neural networks for online translation, for foreign language speaking practice, for foreign language writing practice. The category of additional neural networks includes neural networks for data visualization, generation of timelines, promotion of educational products.