STUDENT DIGITAL FOOTPRINT AS A TOOL FOR ANALYTICALLY-ORIENTED ASSESSMENT OF LEARNING OUTCOMES OF FUTURE MEDICAL PROFESSIONALS
Abstract
DOI: https://doi.org/10.26565/2074-8922-2026-86-32
Purpose. The purpose of the study is to substantiate and develop a model for using a student's digital footprint for a comprehensive assessment of academic achievements in a higher medical education institution.
Methods. The study employed a combination of theoretical and empirical methods. The theoretical methods included analysis, generalization, and systematization of scientific sources on the issues of education digitalization and assessment of academic achievements. The empirical part involved the collection, processing, and interpretation of students’ digital footprint data within a learning management system. The research was conducted among 40 first-year students majoring in “Medicine” during the study of the discipline “Philosophy.” Indicators of digital activity were analyzed, including the frequency of system logins, duration of engagement with educational materials, participation in forum discussions, level of interaction with learning content, as well as the timeliness and completeness of task completion. Additionally, behavioral patterns of students’ learning activities in the digital environment were taken into account. Descriptive statistics, comparative analysis, and elements of correlation analysis were applied to process the results.
Results. The article proposes a model for assessing students’ academic achievements based on their digital footprint, which integrates cognitive, behavioral, communicative, and organizational components. An integral assessment indicator was developed, calculated using an author’s formula, which allows considering both final learning outcomes and the dynamics of educational activity. A statistically significant relationship was found between the level of students’ digital activity and their academic performance. The results confirm the feasibility of using the digital footprint as a tool for analytical assessment.
Conclusions. Using the digital footprint as an assessment tool contributes to increasing the effectiveness of the educational process, ensures continuous monitoring of learning activities, and creates conditions for the individualization of learning. The proposed model has practical value for higher education institutions, as it allows improving the assessment system, increasing its objectivity and validity. Its implementation facilitates evidence-based pedagogical decision-making based on the analysis of educational data.
Downloads
References
Bykov, V. Yu., Spirin, O. M., Pinchuk, O. P. (2020). Modern tasks of digital transformation of education. UNESCO Chair Journal Lifelong Professional Education in the XXI Century, 1, 27–36. https://doi.org/10.35387/ucj.1(1).2020.27-36 (In Ukrainian).
Bilous, P. (2026). Interactive digital learning tools as a means of improving the effectiveness of the educational process. International Scientific Journal of Education and Linguistics, 5(2), 20–25. https://doi.org/10.46299/j.isjel.20260502.03 (In Ukrainian).
Hlazunova, O. H., Klymenko, Ye. O., Voloshyna, T. V., Mokriyev, M. V., Voronenko, O. V. (2024). Educational analytics in universities: Tools for analysis and forecasting. Telecommunications and Information Technologies, (2(83)), 49–59. https://doi.org/10.31673/2412-4338.2024.026171 (In Ukrainian).
Hulai, O., Kabak, V., Herasymchuk, H. (2023). Digital learning tools and technologies: theoretical and practical aspects: monograph. Lutsk: LNTU. https://lib.lntu.edu.ua/sites/default/files/2023-11/%D0%9C%D0%BE%D0%BD%D0%BE%D0%B3%D1%80%D0%B0%D1%84%D1%96%D1%8F%20%D0%93%D1%83%D0%BB%D0%B0%D0%B9%2C%20%D0%9A%D0%B0%D0%B1%D0%B0%D0%BA%2C%20%D0%93%D0%B5%D1%80%D0%B0%D1%81%D0%B8%D0%BC%D1%87%D1%83%D0%BA.pdf (In Ukrainian).
Lakhno, V., Voloshyn, S., Mamchenko, S., Kulynich, O., Kasatkin, D. (2024). Cluster analysis for researching digital footprints of students in educational institutions. Electronic Professional Scientific Journal «Cybersecurity: Education, Science, Technique», 3(23), 31–41. https://doi.org/10.28925/2663-4023.2024.23.3141 (In Ukrainian).
Martynenko, S. (2023). The impact of digitalization on modeling the educational space of a higher education institution. Continuing Professional Education: Theory and Practice, 77(4), 88–96. https://doi.org/10.28925/1609-8595.2023.4.7 (In Ukrainian).
Romanych, I., Panchenko, I., Bundziak, S. (2025). Higher education market of ukraine and the educational smart-technologies. Entrepreneurship and Innovation, (34), 61-68. https://doi.org/10.32782/2415-3583/34.9 (In Ukrainian).
Strutynska, O. (2020). Peculiarities of the modern learners generation under the conditions of the digital society development. Open educational e-environment of modern university, (9), 145–160. https://doi.org/10.28925/2414-0325.2020.9.12 (In Ukrainian).
Tardaskina, T., Reklitska, A. (2023). Modern digital tool for institutions of higher education in the conditions of development EDTECH. Economy and Society, (55). https://doi.org/10.32782/2524-0072/2023-55-51 (In Ukrainian).
Terletska, T. (2025). Model of organisation of differentiated instruction in lms moodle based electronic educational environment. Electronic scientific professional journal “Open educational e-environment of modern university”, (19), 177–190. https://doi.org/10.28925/2414-0325.2025.1912 (In Ukrainian).
Tkachuk, H. V. (2018). Theoretical aspects and the state of implementation of blended learning in higher education institutions of Ukraine. European vector of contemporary psychology, pedagogy and social sciences: the experience of Ukraine and the Republic of Poland: Collective monograph. Volume 1. Sandomierz: Izdevnieciba “Baltija Publishing”. (pp. 465-484). https://dspace.udpu.edu.ua/handle/6789/8666 (In Ukrainian).
Franchuk, N., Novytska, T., Chyzhmotria, O., Shimon, O. (2024). Development of digital competence of research and pedagogical staff using the google analytics 4 information system. Scientific Journal of the Dragomanov Ukrainian State University Series 2 Computer-Oriented Learning Systems, 23 (30), 93-1084. https://doi.org/10.31392/UDU-nc.series2.2024.23(30).09 (In Ukrainian).
Chernovol, Ye. O., Chepeliuk, A. V., Kurtiak, F. F. (2023). Regarding the digitalization of the educational process in higher education institutions in Ukraine: new opportunities and prospects. Academic Visions, (15). https://academy-vision.org/index.php/av/article/view/132 (In Ukrainian).
Shvets, O. V. (2025). Monitoring the quality of education in higher education as a strategic management tool. Pedagogical Academy: Scientific Notes, (22). https://doi.org/10.5281/zenodo.17253781 (In Ukrainian).
Abrahamson, D., Worsley, M., Pardos, Z. A., Ou, L. (2022). Learning analytics of embodied design: Enhancing synergy. International Journal of Child-Computer Interaction, 32, 100409. https://doi.org/10.1016/j.ijcci.2021.100409
Cerro Martínez, J. P., Guitert Catasús, M., Romeu Fontanillas, T. (2020). Impact of using learning analytics in asynchronous online discussions in higher education. International Journal of Educational Technology in Higher Education, 17, article number 39. https://doi.org/10.1186/s41239-020-00217-y
Gašević, D., Tsai, Y.-S., Drachsler, H. (2022). Learning analytics in higher education—Stakeholders, strategy and scale. The Internet and Higher Education, 52, 100833. https://doi.org/10.1016/j.iheduc.2021.100833
Jivet, I., Scheffel, M., Specht, M., Drachsler, H. (2018). License to evaluate: Preparing learning analytics dashboards for educational practice. LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 31–40). https://doi.org/10.1145/3170358.3170421
Khalil, M., Ebner, M. (2015). Learning Analytics: Principles and Constraints. Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2015 (pp. 1326–1336). https://doi.org/10.13140/RG.2.1.1733.2083
Larrabee Sonderlund, A., Hughes, E., Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594–2612. https://doi.org/10.1111/bjet.12720
Lim, L., Dawson, S., Joksimovic, S., & Gašević, D. (2019). Exploring students’ sensemaking of learning analytics dashboards: Does frame of reference make a difference? LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge. pp. 250–259. https://doi.org/10.1145/3303772.3303804
Matcha, W., Ahmad Uzir, N. A., Gašević, D., Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245. https://doi.org/10.1109/TLT.2019.2916802
Mwalumbwe, I., Mtebe, J. S. (2017). Using Learning Analytics to Predict Students’ Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology. The Electronic Journal of Information Systems in Developing Countries, 79(1), 1–13. https://doi.org/10.1002/j.1681-4835.2017.tb00577.x
Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D., Gašević, D. (2020). Learning analytics in European higher education—Trends and barriers. Computers & Education, 155, 103933. https://doi.org/10.1016/j.compedu.2020.103933
Viberg, O., Hatakka, M., Bälter, O., Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027