ANALYSIS OF THE RELATIONSHIPS BETWEEN PSYCHOLOGICAL CHARACTERISTICS AND DIGITAL BEHAVIOR OF STUDENTS IN DISTANCE LEARNING
Abstract
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.
Keywords
Distance learning, psychological characteristics of students, digital learning behavior, paradoxical behavioral patterns, behavioral markers of risk
References
- Abakumova, I., Grishina, A., Zvezdina, G. (2022) Psychological Predictors of Students’ Behavior in Internet Under Information Uncertainty. International Journal of Cognitive Research in Science, Engineering and Education. 10(3), P. 17–26. doi: 10.23947/2334-8496-2022-10-3-17-26
- Alyahyan E., Dustegor D. (2020) Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education. N 17. doi: 10.1186/s41239-020-0177-7
- Atherton M., Shah M., Vazquez J., Griffiths Z., Jackson B., Burgess C. (2017) Using learning analytics to assess student engagement and academic outcomes in open access enabling programmes. Open Learning: The Journal of Open, Distance and e-Learning. 32(2). P. 119–136. doi:10.1080/02680513.2017.1309646
- Belushkina G.V., Vasilchenko M.V., Lopatkina N.A., Khafizyanova O.E. (2013) Ob issledovanii matematicheskoy modeli uspeshnosti obucheniya i sozdanii bazy psikhologicheskikh dannykh dlya aprobatsii modeli [On study of training success mathematical model and of creating psychological database for model testing] Vestnik Donskogo gosudarstvennogo tekhnicheskogo universiteta (Vestnik of Don State Technical University). T. 13. 5-6(74). P. 44–52. doi:10.12737/1279
- Borodovitsyna T.O. (2020) Psikhologicheskoye blagopoluchiye studentov v usloviyakh distantsionnogo obucheniya [Psychological well-being of students in conditions of distance learning]. Mir pedagogiki i psikhologii: mezhdunarodnyy nauchno-prakticheskiy zhurnal (World of Pedagogy and Psychology: International Scientific and Practical Journal). 12(53). P. 192–199. URL: https://scipress.ru/pedagogy/articles/psikhologicheskoe-blagopoluchie-studentov-v-usloviyakh-distantsionnogoobucheniya. html (accessed:10.07.2023).