Analysis of the Relationships Between Psychological Characteristics and Digital Behavior of Students in Distance Learning

The article presents the results of a comprehensive study examining the relationships between students’ psychological characteristics and their digital learning behavior in a distance education system. Using a sample of 912 students, the authors analyzed the influence of motivation, self-regulation, and anxiety on learning activity metrics recorded with the educational platform. A multi-method approach was employed, combining psychometric measurements with machine learning analysis of digital activity traces.The study has identified three types of learning behavior: a group characterized by high engagement with low results, a group with an optimal strategy and a group with intensive but ineffective activity.Paradoxical relationships were found: a positive correlation between the number of completed tasks and dropout risk (β = 1.00), as well as a positive correlation between engagement and risk (β = 0.12). It was established that anxiety indirectly affects academic performance by reducing motivation and engagement.The practical significance of the study lies in establishing empirically derived behavioral markers of academic risk: completion of more than 20 tasks serves as an indicator of perfunctory engagement, an engagement score exceeding 67 points signifies a dysfunctional learning state, while system time surpassing 27.8 hours emerges as a robust marker of procrastinatory behavior. These evidence-based thresholds enable the enhancement of existing early warning systems for student dropout and the development of tailored pedagogical support strategies addressing the specific needs of distinct student types.