DOI: 10.17587/prin.17.391-403

A Tool for Analyzing the Deviation of an IT Specialist Curriculum from a Harmonized Standard

I. I. Sukhikh, Postgraduate Student, vargostelemax@gmail.com, Perm National Research Polytechnic University, Perm, 614990, Russian Federation

Corresponding author: Ilya I. Sukhikh, Postgraduate Student, Perm National Research Polytechnic University, Perm, 614990, Russian Federation E-mail: vargostelemax@gmail.com

This article examines the design principles and implementation approaches for a tool designed to analyze devia­tions of IT specialist curricula from a perfectly harmonized standard. Harmonization refers to the alignment of curriculum structure across substantive, logical, temporal, and methodological parameters, ensuring the integrity of the educational process and the absence of duplication. A mathematical model of a perfectly harmonized curriculum is presented, based on multicriteria optimization, including indicators of discipline continuity, duplication index, and discipline graph cohesion. An experiment was conducted using the example of the 09.03.01 "Informatics and Computer Engineering" program at Perm National Research Polytechnic University. This experiment demonstrated that the proposed model allows for a quantitative assessment of curriculum deviations from the standard and the identification of bottlenecks requiring substantive and organizational correction. The results demonstrate the potential of the proposed approach for improving educational program planning and enhancing their internal consistency.

Keywords: decision support system, curriculum management, IT specialist training, curriculum, harmonization, standard model, knowledge gaps, content duplication, course sequence, continuous learning, Python

pp. 391—403

For citation:
Sukhikh I. I. A Tool for Analyzing the Deviation of an IT Specialist Curriculum from a Harmonized Standard, Programmnaya Ingeneria, 2026, vol. 17, no. 7, pp. 391—403. DOI: 10.17587/prin.17.391-403. (in Russian).

References:

1. Fayzrakhmanov R. A., Dolgova E. V., Sukhikh I. I. Harmonization Status Analysis of 09.03.01 "Computer Science and Com­puter Engineering" Discipline, Bulletin of Perm University. Math­ematics. Mechanics. Computer Science, 2024, no. 1 (64), pp. 53—59. DOI: 10.17072/1993-0550-2024-1-53-59 (in Russian).
2. Sukhikh I. I. Models and algorithms of the system for improving the quality of training of IT-specialists on the basis of curriculum harmonization, Nonlinear world, 2025, vol. 23, no. 1, pp. 35—45. DOI: 10.18127/J20700970-202501-05 (in Russian).
3. Yarullin D. V. Intelligent control system for IT specialists training based on denotative analytics, Applied Mathematics and Control Sciences, 2022, no. 3, pp. 141—164. DOI: 10.15593/24999873/2022.3.08 (in Russian).
4. Sukhikh I. I. Intelligent Curriculum Management System for Training an IT Specialist, Programmnaya Ingeneria, 2026, vol. 17, no. 1, pp. 27—39. DOI: 10.17587/prin.17.27-39 (in Russian).
5. Kuzmina E. A. Models and optimization of curricula in educational systems. Dissertation of Ph. D. Ufa. 2002. 155 p. (in Russian).
6. Naumova S. V. Models and methods for automated synthesis of higher education curricula. Abstract of dissertation of Ph. D. Saratov. 2005. 20 p. (in Russian).
7. Kotov S. S. A model for managing the curriculum of com­petence-oriented educational programs taking into account the preferences of various social groups. Abstract of dissertation of Ph. D. Moscow. 2010. 18 p. (in Russian).
8. Istomin A. L. Methodological foundations for optimal planning of the educational process at a university. Dissertation of D. Sc. Astrakhan. 2012. 332 p. (in Russian).
9. Zykova T. V., Kytmanov A. A., Khalturin E. A. et al. The algorithm for analysis and evaluation of educational programs curricula, Informatics and Education, 2024, vol. 39, no. 1, pp. 52—64. DOI: 10.32517/0234-0453-2024-39-1-52-64 (in Russian).
10. Rubin Yu., Mozhzhukhin D., Alekseeva E. Continuous Entrepreneurship Education: Formation of an Innovative Model (2021 Experience), Journal of Modern Competition, 2021, vol. 15, no. 4, pp. 91—108. DOI: 10.37791/2687-0657-2021-15-4-91-108 (in Russian).
11. Strunina N. V. E-learning resource "BRITAIN AND THE BRITISH" as a didactic tool of formation of intercultural competence at secondary schools and universities, Sovremennye problem nauki i obrazovaniya, 2020, no. 6, pp. 1—8. DOI: 10.17513/spno.30452 (in Russian).
12. Namahoot C. S., Namahoot K. S., Nuntawong C., Suk-ma N. ONTO-ALIGN: An Ontology-based Framework for Evalu­ating Curriculum Alignment with Educational Standards in Computer Science, Interdisciplinary Journal of Information, Knowledge, and Management, 2025, vol. 20, pp. 1—27. DOI: 10.28945/5600.
13. Piriyapongpipat P., Goldin S., Ditcharoen N. An alternative approach to ontology-based curriculum development in higher education, Smart Learn. Environ., 2024, vol. 11, article 20. DOI: 10.1186/s40561-024-00307-8.
14. Dedic F., Bijedic N., Gaspar D. Genetic algorithms as a tool for development of balanced curriculum, Interdisciplinary Description of Complex Systems, 2020, vol. 18, no. 2, pp. 175—193. DOI: 10.7906/indecs.18.2.8.
15. de Blas C., Gomez-Gonzalez D., Criado R. Network analysis: An indispensable tool for curricula design. A real case-study of the degree on mathematics at the URJC (Spain), PLoS ONE, 2021, vol. 16, no. 3, article e0248208. DOI: 10.1371/journal.pone.0248208.
16. Yurchishina M. V. Functional Model of the Optimal Cur­riculum Decision Support System, Russian Journal of Cybernetics, 2025, vol. 6, no. 2, pp. 114—125 (in Russian).
17. Kutenkova E. Y., Larina T. V. Optimizing the curriculum based on production requirements, Aktualnie voprosy obrazovaniya, 2025, no. 1, pp. 101—107. DOI: 10.33764/2618-8031-2025-1-101-107 (in Russian).
18. Murdoch D. Development of an ontology of course design in higher education (Dissertation). Australia, Charles Sturt University, 2024. 310 p.
19. Fayzrakhmanov R. A., Dolgova E. V., Sukhikh I. I. Algo­rithm for Extracting Key Concepts from Educational Programs of IT Specialists using the Hybrid Context Ranking Method, Programmnaya Ingeneria, 2025, vol. 16, no. 7, pp. 334—346. DOI: 10.17587/prin.16.334—346 (in Russian).
20. de Koning B. Extracting Sections From PDF-Formatted CTI Reports, Proceedings of the 37th Twente Student Conference on IT, En-schede, Netherlands, University of Twente, 2022, pp. 1—9.
21. Fantechi A., Gnesi S., Livi S., Semini L. A spaCy-based tool for extracting variability from NL requirements, Proceedings of the 25th ACM International Systems and Software Product Line Conference (SPLC'21), New York, NY, USA, Association for Computing Machinery, 2021, pp. 32—35. DOI: 10.1145/3461002.3473074.
22. Redkina O. Yu., Karpeta T. V., Ponomareva D. S. et al. Machine learning in the adaptation of educational texts: lexical aspect, Bulletin of Chelyabinsk State University, 2024, no. 8 (490), pp. 220—229. DOI: 10.47475/1994-2796-2024-490-8-220-229 (In Russian).
23. Colangelo M. T., Meleti M., Guizzardi S. et al. A comparative analysis of Sentence Transformer models for automated journal recommendation using PubMed metadata, Big Data and Cognitive Computing, 2025, vol. 9, no. 3, article 67. DOI: 10.3390/bdcc9030067.