Digitalization of motivational features as a way to organize independent work of bachelors

Abstract

Problem statement . Effective organization of extracurricular work for future bachelors involves the construction of individual learning trajectories, based, among other things, on the level of motivation to study specialized disciplines. The expediency of using intelligent information technologies in classifying the composition and forms of independent work of bachelors of applied mathematics is substantiated. Methodology. Applying the developed mobile application, multi-parameter motivational characteristics of students were identified. The resulting motivational profiles are clustered into five motivational groups, similar in sign values. The construction and analysis of multi-parameter classification were carried out via cluster analysis and neural network technologies. Clustering of motivational groups and the application of appropriate strategies for organizing independent work were realized at the Faculty of Computer Technologies and Applied Mathematics of Kuban State University. Results. The constructed neural network classifies the bachelor's motivational profile, assigns him a strategy for independent work, defining the specific values of the variable elements of the strategy. Strategies for organizing independent work have been established for clustered motivational groups. Conclusion. Construction and clustering of motivational profiles allows you to adjust individual strategies for independent learning of bachelors of applied mathematics, determine the values of variable elements and, as a result, not only develop professional skills, but also develop skills in organizing your own work process, resource allocation, and identifying work stages.