Research Training and Machine Learning: from Matching to Convergence

Abstract

Empowerment of human capabilities with new technologies that can increase labor productivity is an ever-increasing trend. The exponential rate of progress is demonstrated by artificial intelligence technologies. Existing applied solutions with the use of machine learning in pedagogy are analyzed, ways of its extrapolation to the convergence model of research training and machine learning are shown. As a basic idea, there are ideas generally accepted in the scientific community about the structure of education. The addition of this concept with the possibilities of quantitative content analysis made it possible to clarify the essence of “machine learning”, to substantiate its place among such related semantic concepts as “artificial intelligence” and “neural networks”. The applied systematic approach contributed to the identification of latent links between research training and machine learning, including the importance of a variety of structured and unstructured data on the subjects and objects of research training, the reliability of the data sources used. The SWOT analysis made it possible to substantiate the expediency of introducing and further developing the concept of “digital research profile” as one of the possible options for the convergence of man and machine, as well as to identify promising areas for the development of traditional pedagogical systems based on artificial intelligence.

Keywords

• education digitalization
• digital research profile
• research training
• artificial intelligence
• machine learning
• neural network
• deep learning