Selection criteria for cloud-based learning technologies for the development of professional competencies in statistics bachelors

This article presents the findings of an expert evaluation of current cloud-based learning technologies according to defined criteria and scientifically supports the selection criteria for cloud-based learning technologies for the development of professional competencies of bachelor's degree statistics majors. Information-didactic, functional, and technological criteria were established for the selection of cloud-based learning technologies for the development of professional competences of bachelor's degree statistics majors. The method of expert evaluation was used to implement the choice of cloud-based learning technologies for the formation of professional competences of bachelor's degree statistics majors, and effective use in the process of formation of relevant competencies. The expert evaluation was conducted in two stages: the first stage chose the cloud-based learning technologies that the author deemed to be the most appropriate, and the second stage identified those cloud-based learning technologies that should be used in the educational process as a way to develop professional skills for Bachelor of Statistics graduates. According to the study, CoCalc and Wolfram|Alpha are the cloud-based learning tools that are most suitable, practical, and efficient for the development of professional capabilities of upcoming bachelor's degree recipients in statistics.