Russian Federation
UDC 004.75
The article presents an overview comparative analysis of the means and approaches to building relationships between the components of educational programs. The study used the methods of system analysis and analytical review, comparative analysis, as well as generalization of the identified limitations. The existing approaches to building relationships between the components of educational programs, such as ontological, graph, semantic, and based on natural language processing methods, are analyzed. Ontological models formalize the structure of an educational program, graph models identify interdisciplinary connections, and semantic and based on natural language processing models allow for the automation of matching student learning outcomes, discipline topics, and educational materials. However, these approaches have several limitations, such as dependence on manual markup, limited consideration of the actual content of discipline curricula, and limited analysis within a single course. The study concludes that a comprehensive approach is necessary and provides a foundation for further research on building relationships between the components of educational programs.
educational programs, work program of the discipline, graph model, learning outcomes, semantic analysis, ontology, learning outcomes, curriculum
1. Nazarov E.V. Ontologicheskoe predstavlenie osnovnoj obrazovatel'noj programmy // Vestnik kibernetiki. 2021. № 3 (43). S. 51–59. DOI:https://doi.org/10.34822/1999-7604-2021-3-51-59 EDN VMICXV
2. Bulanova I.A., Pyl'kin A.N. Semanticheskaya model' obrazovatel'noj programmy i programmnoe obespechenie dlya ee postroeniya, vizualizacii i analiza // Vestnik RGRTU. 2023. № 86. S. 133–144. DOI:https://doi.org/10.21667/1995-4565-2023-86-133-144
3. Primenenie silovogo algoritma vizualizacii grafov dlya analiza uchebnyh planov obrazovatel'nyh programm vysshego obrazovaniya / T.V. Zykova [i dr.] // Sovremennye informacionnye tekhnologii i IT-obrazovanie. 2023. T. 19. № 1. S. 104–116. DOI:https://doi.org/10.25559/SITITO.019.202301.104-116 EDN KZHOWJ
4. Timofeev A.N. Ontologicheskij podhod k razrabotke adaptivnoj modeli kompetencij i professional'noj podgotovki v sfere informacionnyh tekhnologij // Intellektual'nye tekhnologii na transporte. 2023. № 1 (35-1). S. 42–47. EDN HKVJSU
5. Automating the mapping of course learning outcomes to program learning outcomes using natural language processing for accurate educational program evaluation / N. Zaki [et al.] // Research Square. 2022. DOI:https://doi.org/10.21203/rs.3.rs-2196467/v1
6. Vesilo R. Automation of curriculum mapping tools: managing the challenges of generic and specific data classifications using large language models and graphical methods // 36th Australasian Association for Engineering Education Annual Conference (AAEE2025): Engineering education: between the human and the digital. Engineers Australia, 2025. DOI:https://doi.org/10.3316/informit.T2026031700006491438511833
7. Gancevska B., Ramanauskaitė S. Mapping Moodle resources to course topics using text similarity methods and expert evaluation // Applied Sciences. 2026. Vol. 16. P. 2039. DOI:https://doi.org/10.3390/app16042039
8. Ontological approach for competency-based curriculum analysis / M. Milosz [et al.] // Heliyon. 2024. Vol. 10. № 7. P. e29046. DOI:https://doi.org/10.1016/j.heliyon.2024.e29046
9. Piriyapongpipat P., Goldin S., Ditcharoen N. An alternative approach to ontology-based curriculum development in higher education // Smart Learning Environments. 2024. Vol. 11. № 1. P. 20. DOI:https://doi.org/10.1186/s40561-024-00307-8
10. CourseKG: an educational knowledge graph based on course information for precision teaching / Y. Li [et al.] // Applied Sciences. 2024. Vol. 14. P. 2710. DOI:https://doi.org/10.3390/app14072710
11. A survey of knowledge graph approaches and applications in education / K. Qu [et al.] // Electronics. 2024. Vol. 13. № 13. P. 2537. DOI:https://doi.org/10.3390/electronics13132537
12. Korneev D.G., Gasparian M.S., Mikryukov A.A. Ontologicheskij podhod k modelirovaniyu innovacionnyh processov na primere raspredelennoj obrazovatel'noj seti vuza // Otkrytoe obrazovanie. 2019. T. 23. № 5. S. 4–13. DOI:https://doi.org/10.21686/1818-4243-2019-5-4-13
13. Smith H., Chittams J. Defining best practices and validation for curriculum mapping // Cogent Education. 2024. Vol. 11. DOI:https://doi.org/10.1080/2331186X.2024.2342662
14. Algoritm analiza i ocenki uchebnyh planov obrazovatel'nyh programm / T.V. Zykova [i dr.] // Informatika i obrazovanie. 2024. T. 39. № 1. S. 52–64. DOI:https://doi.org/10.32517/0234-0453-2024-39-1-52-64 EDN UNSWXG
15. Kuz'mina E.A., Nizamova G.F. Formirovanie uchebnogo plana na osnove grafovoj modeli // Informatika i obrazovanie. 2020 № 5. S. 33–43. DOI:https://doi.org/10.32517/0234-0453-2020-35-5-33-43
16. Wengle E., Knorn S., Varagnolo D. COnCUR – COherence in CURricula: a tool to assess, analyze and visualize coherence in higher education curricula // IFAC-PapersOnLine. 2020. Vol. 53. P. 17598–17603. DOI:https://doi.org/10.1016/j.ifacol.2020.12.2675
17. Graph-theoretic approaches and tools for quantitatively assessing curricula coherence / D. Varagnolo [et al.] // European Journal of Engineering Education. 2021. Vol. 46. № 3. P. 344–363. DOI:https://doi.org/10.1080/03043797.2019.1710465
18. Wang Y., Zhan Z., Wang H. Network analysis of outcome-based education curriculum system: a case study of environmental design programs in medium-sized cities // Sustainability. 2025. Vol. 17. P. 7091. DOI:https://doi.org/10.3390/su17157091
19. Krishnan S., Rajendran S., Zakariah M. A secured accreditation and equivalency certification using Merkle mountain range and transformer-based deep learning model for the education ecosystem // Scientific Reports. 2025. Vol. 15. P. 22511. DOI:https://doi.org/10.1038/s41598-025-06789-x
20. Dyshenov B.A., Najhanova L.V., Shirapov D.Sh. Modul' podgotovki kollekcii rabochih programm dlya latentno-semanticheskogo analiza // Fundamental'nye issledovaniya. 2017. № 2. S. 57–61. EDN YGGCKV
21. Minaev D.V. Podhody k proektirovaniyu i aktualizacii kompetentnostnyh modelej obrazovatel'nyh programm na osnove intellektual'nogo analiza vakansij rabotodatelej // Upravlencheskoe konsul'tirovanie. 2023. № 10 (178). S. 45–68. DOI:https://doi.org/10.22394/1726-1139-2023-10-45-68 EDN QXMAGP
22. Aleksandrov A.S., Zaripova V.M. Analiz trebovanij k IT-specialistam na osnove vakansij, obrazovatel'nyh standartov i predpochtenij studentov s primeneniem bol'shih yazykovyh modelej // Computational Nanotechnology. 2025. T. 12. № 5. S. 80–94. DOI:https://doi.org/10.33693/2313-223X-2025-12-5-80-94 EDN ELOENB
23. Abu-Salih B., Alotaibi S. A systematic literature review of knowledge graph construction and application in education // Heliyon. 2024. Vol. 10. № 3. P. e25383. DOI:https://doi.org/10.1016/j.heliyon.2024.e25383
24. Fettach Y., Ghogho M., Benatallah B. Knowledge graphs in education and employability: a survey on applications and techniques // IEEE Access. 2022. Vol. 10. P. 1–1. DOI:https://doi.org/10.1109/ACCESS.2022.3194063



