Behavioral indicators of learner retention in digital educational services: evidence from online platforms for state exam preparation
The article addresses learner retention in digital educational services, focusing on online platforms that prepare school students for Russian state examinations. The relevance of the topic is related to the slowdown of the Russian EdTech market and to the growing importance of repeat purchases, subscription renewal and the quality of support provided to already attracted users. The paper is methodological and conceptual: it does not test causal relationships empirically but systematizes behavioral indicators of retention and the managerial limitations of their use. Retention is treated as a dual result: the preservation of regular learner participation in educational activity and the continuation of contractual relations with the payer of the service. The article identifies groups of indicators related to intensity of engagement, rhythm of self-regulated work, response to task difficulty, interaction with feedback and subject progress. It also proposes an example of indicator operationalization that should be adjusted using data from a particular platform, and outlines the economic assessment of service interventions through retention, churn, additional revenue, support costs and return on intervention. The paper argues that behavioral indicators should not be interpreted as automatic diagnostics of motivation, because they describe observable digital behavior rather than the internal causes of learning difficulties. The practical contribution is a service-oriented support framework linking digital signals with acceptable platform actions.
Takhautdinov, A. R. (2026), “Behavioral indicators of learner retention in digital educational services: evidence from online platforms for state exam preparation”, Re-search Result. Business and Service Technologies, 12 (3), pp. 143-153. DOI: 10.18413/2408-9346-2026-12-3-1-0
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Ascarza, E. et al. (2018), “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions”, Customer Needs and Solutions, Vol. 5, 1-2, pp. 65-81. DOI: 10.1007/s40547-017-0080-0.
Avdeeva, S. M. et al. (2017), Individualization of learner educational activity based on e-learning using distance education technologies: a practical guide, Federal Institute for Education Development, Moscow, Russia, 121 p. (In Russ.).
Bañeres, D., Rodríguez-González, M. E. and Guerrero-Roldán, A. E. (2023), “An Early Warning System to Identify and Intervene Online Dropout Learners”, International Journal of Educational Technology in Higher Education, Vol. 20, article 3. DOI: 10.1186/s41239-022-00371-5.
Bergdahl, N. et al. (2024), “Unpacking Student Engagement in Higher Education Learning Analytics: A Systematic Review”, International Journal of Educational Technology in Higher Education, Vol. 21, article 63. DOI: 10.1186/s41239-024-00493-y.
Chiu, T. K. F. (2022), “Applying the Self-Determination Theory (SDT) to Explain Student Engagement in Online Learning during the COVID-19 Pandemic”, Journal of Research on Technology in Education, Vol. 54, Sup. 1, pp. S14-S30. DOI: 10.1080/15391523.2021.1891998.
Chong, K. T. et al. (2025), “A Systematic Review of Machine Learning Techniques for Predicting Student Engagement in Higher Education Online Learning”, Journal of Information Technology Education: Research, Vol. 24, article 5. DOI: 10.28945/5456.
Chudinovskikh, M. V. (2022), “Prospects for the development of the EdTech market in Russia”, Baikal Research Journal, Vol. 13, 4, article 13. DOI: 10.17150/2411-6262.2022.13(4).13. (In Russ.).
Ferguson, R. (2012), “Learning Analytics: Drivers, Developments and Challenges”, International Journal of Technology Enhanced Learning, Vol. 4, 5/6, pp. 304-317. DOI: 10.1504/IJTEL.2012.051816.
Kizilcec, R. F., Piech, C. and Schneider, E. (2013), “Deconstructing Disengagement: Analyzing Learner Subpopulations in Massive Open Online Courses”, Proceedings of the Third International Conference on Learning Analytics and Knowledge, ACM, New York, USA, pp. 170-179. DOI: 10.1145/2460296.2460330.
Konanchuk, D. S. (2013), “EdTech: a new technological platform in education”, University Management: Practice and Analysis, 5 (87),
pp. 65-73 [Online], available at: https://www.umj.ru/jour/article/view/473 (Accessed 10 June 2026). (In Russ.).
Kononykhina, O. V. (2021), “Student motivation in distance learning”, International Journal of Humanities and Natural Sciences, 2-1 (53), pp. 107-111. DOI: 10.24412/2500-1000-2021-2-1-107-111. (In Russ.).
Lee, J. and Kim, D. (2025), “From Awareness to Empowerment: Self-Determination Theory-Informed Learning Analytics Dashboards to Enhance Student Engagement in Asynchronous Online Courses”, Journal of Computing in Higher Education, Vol. 37, 3, pp. 1078-1118. DOI: 10.1007/s12528-024-09416-2.
Martin, F. and Bolliger, D. U. (2018), “Engagement Matters: Student Perceptions on the Importance of Engagement Strategies in the Online Learning Environment”, Online Learning, Vol. 22, 1, pp. 205-222. DOI: 10.24059/olj.v22i1.1092.
Ojo, A. O. et al. (2024), “Investigating Student’s Motivation and Online Learning Engagement through the Lens of Self-Determination Theory”, Journal of Applied Research in Higher Education, Vol. 16, 5, pp. 2185-2198. DOI: 10.1108/JARHE-09-2023-0445.
Ryan, R. M. and Deci, E. L. (2000), “Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being”, American Psychologist, Vol. 55, 1, pp. 68-78. DOI: 10.1037/0003-066X.55.1.68.
Siemens, G. and Long, P. (2011), “Penetrating the Fog: Analytics in Learning and Education”, EDUCAUSE Review, Vol. 46, 5, pp. 30-40 [Online], available at: https://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education (Accessed 10 June 2026).
Smart Ranking (2026a), “EdTech market grew by 12% in 2025”, Smart Ranking [Online], available at: https://smartranking.ru/ru/analytics/edtechs/edtech-rynok-vyros-v-2025-godu-na-12/ (Accessed 10 June 2026). (In Russ.).
Smart Ranking (2026b), “Revenue of online exam preparation schools exceeded RUB 19 billion”, Smart Ranking [Online], available at: https://smartranking.ru/ru/analytics/edtechs/vyruchka-shkol-onlajn-podgotovki-k-ekzamenam-prevysila-19-mlrd-rublej/ (Accessed 10 June 2026). (In Russ.).
TASS (2024), “Mediascope reports that Russians over 12 spend 4.5 hours a day online”, TASS [Online], available at: https://tass.ru/obschestvo/21268123 (Accessed 10 June 2026). (In Russ.).
Veretin, R. S. (2025), “Analysis of the effectiveness of digital educational platforms with artificial intelligence support in Russian schools”, MCU Journal of Informatics and Informatization of Education, 3 (73), pp. 7-19. DOI: 10.24412/2072-9014-2025-373-7-19. (In Russ.).