Profiles of Educational Activity of Students Studying Using Distance Learning Technologies in a Digital Educational Environment

The article is devoted to the study of the educational activity of students studying using distance learning technologies. The study is at the intersection of psychology, pedagogy and big data analytics, offering a new look at the problem of effective organization of distance learning. The purpose of the work was to identify latent profiles of students based on the analysis of their "digital traces" in the electronic educational environment. For this purpose, the method of latent profile analysis was used, which allows, based on a set of empirical data, to identify latent classes or population profiles with a similar set of characteristics. The study involved 506 psychology students studying at the distance education department of the Moscow Institute of Psychoanalysis. Digital traces of student activity recorded over 100 days in the spring semester were analyzed. As a result, four models were built, including three, four, five and six classes of students. Based on the fitness indicators, a model of six classes was selected: academic leaders, deep debtors, occasional/dropouts, stable average students, unstable risk takers, and passive observers. These classes differ in the level and nature of activity in the electronic educational environment, as well as in academic performance. Apparently, these classes reflect stable strategies of learning and self-regulation of students. The two most common of them are stable balanced activity and more passive observation. The developed model opens up opportunities for studying self-regulation processes in the learning process. It can also find practical application in the work of educational departments of the university.