Automatic Correction of Individual Educational Trajectory in Adaptive Learning Systems Based on a Composite Approach
Main Article Content
Abstract
In the context of the expanding use of adaptive courses in higher education, the problem of early identification of underperforming students and automatic adjustment of their individual educational trajectory becomes increasingly relevant. Existing methods for identifying underperforming students do not provide an in‑depth analysis of the individualized learning approach employed by the student. The generalized adaptive learning algorithms currently in use do not directly address the task of identifying underperforming students, which may lead to their late detection – by which time it is no longer possible to adjust the students’ approach to learning to ensure successful course completion. The identified problem requires an immediate solution.
The aim of the study was to develop algorithms for identifying underperforming students in adaptive learning systems using a composite approach – based on analysing the individualized learning approach and enabling automatic adjustment of the individual educational trajectory to ensure successful course completion.
The developed algorithms detect student underperformance in mastering an academic discipline by predicting the outcomes of individualized learning using a domain model. In addition, algorithms for the automatic adjustment of the individual educational trajectory have been proposed. Unlike existing approaches, these algorithms generate an individualized educational strategy to ensure successful completion of the course. These algorithms can be integrated into existing adaptive learning systems to increase the proportion of students who successfully complete adaptive courses.
Article Details
References
2. Whitehill J., Mohan K., Seaton D., Rosen Y., Tingley D. Delving Deeper into MOOC Student Dropout Prediction, arXiv:1702.06404 [cs.AI], 2017. https://doi.org/10.48550/arXiv.1702.06404
3. Dass S., Gary K., Cunningham J. Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model // Information. 2021. Vol. 12, 476. https://doi.org/10.3390/info12110476
4. Anh B.N., Giang N.H., Hai N.Q., Minh T.N., Son N.T., Chien B.D. An University Student Dropout Detector Based on Academic Data // 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), Kuala Lumpur, Malaysia, 2023. P. 1–8. https://doi.org/10.1109/ISIEA58478.2023.10212223
5. Shohag S.I., Bakaul M.A. Machine Learning Approach to Detect Student Dropout at University // International Journal of Advanced Trends in Computer Science and Engineering. 2021. Vol. 10, No. 6. https://doi.org/10.30534/ijatcse/2021/041062021
6. Oancea B., Dragoescu R., Ciucu S. Predicting Students’ Results in Higher Education Using Neural Networks // International Conference on Applied Information and Communication Technologies (AICT2013), 25–26 April, 2013, Jelgava, Latvia https://llufb.llu.lv/conference/AICT/2013/Oancea_AICT2013.pdf (date of appeal: 25.06.2026)
7. Leonov A.G., Matyushin M.A., Dyachenko M.S. А neural network-based approach to a priori assessment of the student academic performance in the Мirera digital learning platform // Uspekhi kibernetiki. 2021. Vol. 2, No. 4. P. 49–59. https://doi.org/10.51790/2712-9942-2021-2-4-5
8. Mashchenko K.A. Signature Methods for Time Series Analysis. Russian Digital Libraries Journal. 2025. Vol. 28, No. 3. P. 681-700. (In Russ.). https://doi.org/10.26907/1562-5419-2025-28-3-681-700
9. Diachenko M.S., Leonov A.G. Solving the problem of automating the learning process through experimental search for an individual educational trajectory // Informatics and education. 2024. Vol. 39, No. 4. P. 14–26 (In Russ.). EDN: BMEVBI. https://doi.org/10.32517/0234-0453-2024-39-4-14-26
10. Karmanova E.V., Zakharova Ya.M., Kiselev A.V. Educational data mining in a university using LMS Moodle // Informatics and Education. 2024. Vol. 39, No. 3. P. 16–28. (In Russ.) https://doi.org/10.32517/0234-0453-2024-39-3-16-28
11. Leonov A.G., Mashchenko K.A., Tarasova E.Yu. Novaya arkhitektura obrazovatel’nykh platform v epokhu tsifrovoi transformatsii: paradigmy nolms i smart lms // Trudy NIISI. 2025. T. 15, № 2. S. 54–59. (In Russ.) https://doi.org/10.25682/NIISI.2025.2.0007
12. Dahal, P., Nugroho, S., Schmidt, C., Sänger, V. AI-Based Learning Recommendations: Use in Higher Education // Future Internet. 2025. Vol. 17, No. 7. https://doi.org/10.3390/fi17070285
13. Vasil’ev I.A., Karavaeva A.S., Leonov A.G. et al. O novovvedeniyakh v tsifrovoi obrazovatel’noi platforme Mirera // Trudy NIISI RAN. 2023. T. 13, № 4. S. 127–134. (In Russ.). https://doi.org/10.25682/NIISI.2023.4.0018
14. Vainshtein Yulia V. Pedagogical Design of Personalized Adaptive Subject Learning for University Students in the Context of Digitalization. Doctoral dissertation, Siberian Federal University, 2021 (In Russ.).
15. Krechetov I.A., Romanenko V.V. Implementing the adaptive learning techniques // Educational Studies. Moscow. 2020. No. 2. P. 252–277 (In Russ.). EDN: KYNIIH. https://doi.org/10.17323/1814-9545-2020-2-252-277
16. Piech C., Bassen J., Huang J. et al. Deep Knowledge Tracing // Proc. 28th Int. Conf. Neural Information Processing Systems (NIPS 2015)). 2015. No. 1. P. 505–515
17. AdaptKT: A Domain Adaptable Method for Knowledge Tracing / S. Cheng, Q. Liu, E. Chen et al. // Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining: WSDM ’22. New York, NY, USA: Association for Computing Machinery, 2022. AdaptKT. P. 123–131.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Presenting an article for publication in the Russian Digital Libraries Journal (RDLJ), the authors automatically give consent to grant a limited license to use the materials of the Kazan (Volga) Federal University (KFU) (of course, only if the article is accepted for publication). This means that KFU has the right to publish an article in the next issue of the journal (on the website or in printed form), as well as to reprint this article in the archives of RDLJ CDs or to include in a particular information system or database, produced by KFU.
All copyrighted materials are placed in RDLJ with the consent of the authors. In the event that any of the authors have objected to its publication of materials on this site, the material can be removed, subject to notification to the Editor in writing.
Documents published in RDLJ are protected by copyright and all rights are reserved by the authors. Authors independently monitor compliance with their rights to reproduce or translate their papers published in the journal. If the material is published in RDLJ, reprinted with permission by another publisher or translated into another language, a reference to the original publication.
By submitting an article for publication in RDLJ, authors should take into account that the publication on the Internet, on the one hand, provide unique opportunities for access to their content, but on the other hand, are a new form of information exchange in the global information society where authors and publishers is not always provided with protection against unauthorized copying or other use of materials protected by copyright.
RDLJ is copyrighted. When using materials from the log must indicate the URL: index.phtml page = elbib / rus / journal?. Any change, addition or editing of the author's text are not allowed. Copying individual fragments of articles from the journal is allowed for distribute, remix, adapt, and build upon article, even commercially, as long as they credit that article for the original creation.
Request for the right to reproduce or use any of the materials published in RDLJ should be addressed to the Editor-in-Chief A.M. Elizarov at the following address: amelizarov@gmail.com.
The publishers of RDLJ is not responsible for the view, set out in the published opinion articles.
We suggest the authors of articles downloaded from this page, sign it and send it to the journal publisher's address by e-mail scan copyright agreements on the transfer of non-exclusive rights to use the work.