Among innovative e-learning approaches in the sphere of digital economy and logistics, there is a special focus on artificial intelligence technologies (AI), which, due to their capacity and efficiency in usage, have a significant potential for the development and to some extent are optimal IT tools. The objective of a study is to define an optimum IT software for the organization of massive open online courses (MOOC) in digital economy and digital logistics in the framework of training economics students. Authors have conducted a survey in terms of In-ternet use for education and self-education. The sampling volume makes up 1 600 respondents in at least 80 regions of the Russian Federation. The respondents are divided into four age groups: 18-24 years old, 25-39 years old, 40-54 years old, 55 years old and older. The study uses data from the survey conducted by KMDA.PRO related to digital transformation of 700 representatives from more than 300 Russian companies out of 15 industries and the results of in-depth in-terviews of four categories of employees: top managers, heads of units, mid-level managers and other employees. The study results testify to the need for trans-forming e-learning approaches, taking into account the new labor market re-quirements for training specialists in digital logistics and gaining respective skills such as an active training, coordination, negotiation skills, teaching others, infor-mation literacy, customer focus, oral communication, ability to solve complex is-sues, operational literacy, time management. The use of the research results in practice is possible in case of the organization of online training courses for eco-nomics students in the framework of the higher educational system
This paper studies the patterns of learning behaviour in connection with educational achievement in multi-year undergraduate data science minor specialisation for non-STEM students. In particular, this work focuses on analysing the predictors of academic achievement in blended-learning setting factors related to initial mathematics knowledge, specific traits of educational programs, online and offline learning engagement, and connections with peers. Robust linear regression and non-parametric statistical tests reveal a significant gap in the achievement of students from different educational programs and on the connection between their class attendance and achievement. The results indicate that achievement is not related to the communication on the Q&A forum while peers do affect academic success.
Social science faces tremendous growth of available data about social phenomena on the Internet; however, social science students are usually not prepared to challenges and opportunities of analyzing online data. One of the areas where this growth is especially important is social studies of consumption. In this article we discuss a prototype of the visualization tool intended to support learning netnographic analysis with computational tools
The paper addresses the questions of data science education of current importance. It aims to introduce and justify the framework that allows flexibly evaluate the processes of a data expedition and a digital media created during it. For these purposes, the authors explore features of digital media artefacts which are specific to data expeditions and are essential to accurate evaluation. The rubrics as a power but hardly formalizable evaluation method in application to digital media artefacts are also discussed. Moreover, the paper documents the experience of rubrics creation according to the suggested framework. The rubrics were successfully adopted to two data-driven journalism courses. The authors also formulate recommendations on data expedition evaluation which should take into consideration structural features of a data expedition, distinctive features of digital media, etc.