2026, issue 1, p. 28-35
Received 29.10.2025; Revised 30.11.2025; Accepted 03.03.2026
Published 27.03.2026; First Online 31.03.2026
https://doi.org/10.34229/2707-451X.26.1.3
Previous | FULL TEXT (PDF) | Next
Open Access under CC BY-NC 4.0 License
On the Formation of the Class Schedule at Universities with the Bologna Credit-Modular System of Education
Reshad Ismibayli 1
, Sona Rzayeva 2 * ![]()
1 University of Architecture and Construction, Baku, Azerbaijan
2 Institute of Control Systems, Baku, Azerbaijan
* Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.
Introduction. An integral part of the Bologna Process is the credit-modular system in education (ECTS – European Credit Transfer System). Under the credit-modular education system, which underlies the Bologna Process, two levels of scheduling are created: the teacher's schedule and the student's schedule. Each student develops their own educational trajectory, creating a personal schedule based on their capabilities, preferences, and wishes, taking into account class times.
The schedule of teachers in the credit-modular education system also has its own characteristics compared to the "traditional" education system. This is due to both the modularity principle and the division of subjects into blocks (compulsory subjects; elective subjects within the studied specialty; subjects, freely chosen by students , (the study of which is not mandatory for a specific specialty). However, the most important indicators in assessing the schedule in the credit-modular education system are the problems of filling temporary study groups and the ability of students to register for their chosen courses.
Purpose. To develop and describe a process for assessing the quality of a class schedule, taking into account all soft requirements and additional factors, and to provide a mathematical model for it.
Results. The proposed methodology, mathematical model, and algorithm for assessing schedule quality take into account soft requirements (faculty preferences, workload uniformity, minimizing gaps, etc.) and additional factors, allowing for a quantitative determination of the degree of schedule optimality at universities with credit-modular learning systems.
Conclusions. The proposed approach to assessing the quality of class schedules at universities with the Bologna (credit-modular) learning process reveals several important points regarding their effectiveness and avenues for improvement. The most critical issues in developing university class schedules that impact the quality of the educational process are identified and described.
Particular attention is paid to two key aspects that frequently arise when planning the educational process at credit-based universities: the formation of study groups and course availability. Incomplete study groups can disrupt the educational process and lead to an inefficient allocation of resources such as faculty and classrooms, while students are often unable to enroll in desired courses. This also leads to empty time slots that could be used for more in-demand courses. Such problems can increase the workload of faculty, administrative staff, and students, creating unnecessary scheduling conflicts and reducing the overall effectiveness of the educational process.
The study proposes a multi-criteria approach to schedule quality assessment using penalty coefficients. This methodology offers a way to quantify various violations, such as faculty overload and underload, student dissatisfaction, and course availability issues. By considering these factors holistically, the proposed assessment system provides a more holistic approach to evaluating the analyzed schedule. The approach helps identify areas where the schedule may be suboptimal and provides actionable recommendations for improving the planning process.
Keywords: class schedule, credit-modular system, schedule assessment, hard requirements, soft requirements, additional factor, genetic algorithm.
Cite as: Ismibayli R., Rzayeva S. On the Formation of the Class Schedule at Universities with the Bologna Credit-Modular System of Education. Cybernetics and Computer Technologies. 2026. 1. P. 28–35. https://doi.org/10.34229/2707-451X.26.1.3
References
1. European Commission / EACEA / Eurydice, 2024. The European Higher Education Area in 2024. Bologna Process Implementation Report. Luxembourg: Publications Office of the European Union.
2. Mammadova G., Ismibayli R., Rzayeva S. “Schedule” System for Universities Under the Bologna Education Process. In: Mammadova G., Aliev T., Aida-zade K. (eds). Information Technologies and Their Applications. 2025. ITTA 2024. Communications in Computer and Information Science. 2025. Vol. 2226. Springer. Cham. https://doi.org/10.1007/978-3-031-73420-5_3
3. Aida-zade K., Ismibayli R.E., Rzayeva S. Automated Schedule System for Universities under the Bologna Education Process. Cybernetics and Computer Technologies. 2024. 1. P. 75–90. https://doi.org/10.34229/2707-451X.24.1.6.
4. UniTime. University Timetabling – Comprehensive Academic Scheduling Solutions. 2023. https://www.unitime.org/ (accessed: 29.10.2025)
5. Fedorchenko I., Oliinyk A., Zaiko T., Miedviediev K., Fedorchenko Y., Khokhlov M. Development of a modified genetic method for automatic university scheduling. CEUR Workshop Proceedings. 2024. 3662. P. 210–222.
6. Sakaliuk O., Trishyn F. Analysis of process creation of the courses timetabling. Automation of Technological and Business Processes. 2019. 11 (2). P. 30–35. https://doi.org/10.15673/atbp.v11i2.1370
7. Dvirna O., Verhal K., Ivanov Y. The higher educational information system: management of the timetable scheduling and logistics of the educational process. Systemy upravlinnia ta zv`uaku. 2023. No. 3. P. 86–92. https://doi.org/10.26906/SUNZ.2023.3.086
8. Dunke F., Nickel S. A matheuristic for customized multi-level multi-criteria university timetabling. Ann Oper Res. 2023. 328. P. 1313–1348. https://doi.org/10.1007/s10479-023-05325-2
9. Haneen A., Wasakorn L. A Mathematical Model for Course Timetabling Problem With Faculty-Course Assignment Constraints. IEEE Access. 2021. P. 1–1
10. Christou I.T., Vagianou E., Vardoulias G. Planning courses for student success at the American college of Greece. INFORMS J Appl Anal. 2024. https://doi.org/10.1287/inte.2022.0083
11. Bashab A., Ibrahim A.O., Hashem I.A.T., Aggarwal K., Mukhlif F. et al. Optimization Techniques in University Timetabling Problem: Constraints, Methodologies, Benchmarks, and Open Issues. Computers, Materials & Continua. 2023. 74 (3). P. 6461–6484. https://doi.org/10.32604/cmc.2023.034051
12. Pinedo M. Scheduling: Theory, Algorithms, and Systems. New York, NY, USA : Springer, 2008.
13. Choo E.U., Schoner B., Wedley W.C. Interpretation of criteria weights in multicriteria decision making, Computers & Industrial Engineering. 1999. V. 37, Iss. 3. P. 527–541. https://doi.org/10.1016/S0360-8352(00)00019-X
14. Li T., Sun J., Fei L. Application of Multiple-Criteria Decision-Making Technology in Emergency Decision-Making: Uncertainty, Heterogeneity, Dynamicity, and Interaction. Mathematics. 2025. Vol. 13, Iss. 5. P. 731. https://doi.org/10.3390/math13050731
15. Ismibayli R., Rzayeva S. University Scheduling System on the Bologna Form of Education. 5th International Conference on Problems of Cybernetics and Informatics (PCI), Baku, Azerbaijan. 2023. P. 1–5. 10.1109/PCI60110.2023.10326003
16. Schindl D. Optimal student sectioning on mandatory courses with various sections numbers. Ann Oper Res. 2019. Vol. 275. P. 209–221. https://doi.org/10.1007/s10479-017-2621-1
17. Amin H. A survey of approaches for university course timetabling problem. Computers & Industrial Engineering. 2015. 86. https://doi/org/10.1016/j.cie.2014.11.010
18. Mirani O., Amril A., Toni B. Courses timetabling problem by minimizing the number of less preferable time slots. IOP Conference Series Materials Science and Engineering. 2017. 166. 012025. https://doi/org/10.1088/1757-899X/166/1/012025
19. Francis D., Catalin T. Optimizing the Scheduling of Teaching Activities in a Faculty. Applied Sciences. 2024. 14. 9554. https://doi/org/10.3390/app14209554
20. Allsopp G.L., Wooding S.E., West J.M., Turner A.I. Optimizing assessment workload and student experience: a quantitative and qualitative analysis of an undergraduate subject restructure. Advances in Physiology Education. 2025. Vol. 49, No. 1. P. 154–162. https://doi/org/10.1152/advan.00095.2024
21. Davison M., Kheiri A., Zografos K.G. Modelling and solving the university course timetabling problem with hybrid teaching considerations. J Sched. 2025. 28. P. 195–215. https://doi.org/10.1007/s10951-024-00817-w
22. Arratia-Martinez N.M., Maya-Padron C., Avila-Torres P.A. University Course Timetabling Problem with Professor Assignment. Mathematical Problems in Engineering. 2021. P. 1–9. https://doi/org/10.1155/2021/6617177
23. Seba S., Aparna B. Incorporating Teacher's Preferences and Student Time Management in University Course Timetabling. 2021. 13. 001–009.
24. Félix-Antoine F., François-Michel D.R., Gardner M.A., Marc P., Christian G. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, Machine Learning Open Source Software. 2012. 13. 2171–2175.
25. Eyal V. Hands-On Genetic Algorithms with Python. 2nd ed. Birmingham: Packt Publishing, 2024. 418 p.
ISSN 2707-451X (Online)
ISSN 2707-4501 (Print)
Previous | FULL TEXT (PDF) | Next