Optimization of the automated load distribution process
Students Name: Korzhov Volodymyr Viktorovych
Qualification Level: magister
Speciality: Information Technology Design
Institute: Institute of Computer Science and Information Technologies
Mode of Study: full
Academic Year: 2025-2026 н.р.
Language of Defence: ukrainian
Abstract: Korzhov V.V., Melnyk M.R. (Supervisor). Optimization of the Automated Academic Load Distribution Process. Master’s Thesis. – Lviv Polytechnic National University, Lviv, 2025. In the context of the digital transformation of higher education and the constant growth of information flow volumes, effective management of the educational process becomes a critical factor for the successful activity of higher education institution departments. A key component of this management is the process of planning and distributing the teaching load, which is characterized by high complexity, a multifactorial nature, and dynamic changes. Traditional approaches to data processing, based on the manual transfer of information from disparate spreadsheet files to local databases, are outdated, laborintensive, and accompanied by a high risk of mechanical errors (duplication of disciplines, incorrect identification of teachers, data loss). The relevance of this work is determined by the need to create specialized software that provides automated integration between the university’s centralized information systems and departmental-level subsystems, guaranteeing data integrity, processing efficiency, and the optimization of administrative staff working time. The object of research is the process of planning, accounting, and distributing the teaching load of the scientific and pedagogical staff of a higher education institution department. The main goal of the master’s thesis is to increase the efficiency and reliability of teaching load distribution through the development and software implementation of algorithms for the automated import of data from external sources (Excel formats). The main emphasis is placed on ensuring data integrity, the intelligent identification of personnel, and the resolution of conflicts during record duplication, which allows for minimizing operator errors and reducing information processing time. 8 Scientific novelty: The process of automated import and distribution of teaching load within a relational database environment has been improved through the development and application of a combined algorithm for the syntactic analysis (parsing) of unstructured Excel data and the intelligent matching of text identifiers. This allowed for the automated processing of specific records (including apostrophes and discipline duplicates), the elimination of data loss risks due to key integrity violations, and a reduction in the preparation time of input information for load distribution. The master’s thesis is presented on 61 pages of A4 format, contains 3 chapters, 46 figures, 6 sheets of graphic material, and a list of references containing 26 titles. Keywords: automation, teaching load, information system, database, relational model, VBA, Microsoft Access, data import, syntactic analysis, data integrity. References: 1. Zhezhnych P., Berezko O., Zub K., Demydov I. Analysis of Features and Abilities of Online Systems and Tools Meeting Information Needs of HEIs’ Entrants. CEUR Workshop Proceedings. 2020. Vol. 2616. P. 76–85. URL: http://ceur-ws.org/Vol2616/paper7.pdf. 2. Khamdamov, Utkir, et al. "Conceptual model of the education management information system for higher education institutions." International Journal of Advanced Trends in Computer Science and Engineering 9.5 (2020). 3. Baranova, Evgenia, German Shvetsov, and Tatiana Noskova. "Educational Data Mining Methods for the Analysis of Student’s Digital Footprint." CEUR Workshop Proceedings. Vol. 2920. 2021. 4. Serhani, Mohamed Adel, et al. "Automated system for evaluating higher education programs." Education and Information Technologies 24.5 (2019): 3107-3128. 5. Zhang, Bo, and Guangzhao Yang. "Research on the Application of Intelligent Decision Support System in the Optimal Allocation of Higher Education Resources." 2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2024.