Factors and Barriers to Digital Educational Environment Acceptance by Teachers of Russian and Belarusian Universities: Classification Trees
Abstract
The purpose of this study was to determine the psychological characteristics that hinder or promote the acceptance of the new digital reality by university teachers. Such characteristics included personality traits (Big Five), self-regulation resources (self-activation and self-regulation) and experiences in professional activities. The study involved 308 teachers from various universities in the Russian Federation (71% female, average age 46 ± 11 years) and 265 teachers from various universities in the Republic of Belarus (76% female, average age 45.0 ± 11.5 years). The results of statistical analysis (classification trees) show that for university teachers (both Russian and Belarusian) the most important psychological factors for the acceptance of education digitalization are experiences during professional activities in the digital educational environment, and in second place are self-regulation resources. Developed self-regulation can reduce the effect of negative experiences though. Personality traits do not have a significant impact on attitudes towards working in the digital educational environment.
General Information
Keywords: university digital educational environment, form of education, AUDEE scale, teachers, university, personality traits, Big Five, self-activation, self-regulation, experiences in activity
Publication rubric: Intelligent Technologies in Humanities and Education
Article type: theses
Funding. The research project «Psychological factors of the educational effectiveness in a university digital educational environment» is being implemented by Moscow State University of Psychology & Education within the framework of the strategic academic leadership program «Priority 2030»
For citation: Sorokova M.G., Radchikova N.P., Kozyreva N.V. Factors and Barriers to Digital Educational Environment Acceptance by Teachers of Russian and Belarusian Universities: Classification Trees. Digital Humanities and Technology in Education (DHTE 2023),, pp. 402–413.
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