INTELLIGENT TECHNOLOGY FOR OPTIMIZING THE PROJECT-BASED APPROACH TO TEACHING STUDENTS USING LEARNING MANAGEMENT SYSTEMS

• Published in: Vestnik Nacionalʹnogo tehničeskogo universiteta "HPI". Seriâ Sistemnyj analiz, upravlenie i informacionnye tehnologii (Online) no. 1 (13); pp. 125 - 130
• Main Authors: Sokol, Volodymyr, Godlevskyi, Mykhaylo, Bilova, Mariia, Tupkalenko, Roman
• Format: Journal Article
• Language: English
• Published: National Technical University Kharkiv Polytechnic Institute 11.07.2025
• Subjects: artificial neural network; backpropagation algorithm; effective learning trajectory; learning management system; learning process; multiagent modeling
• DOI: 10.20998/2079-0023.2025.01.19
• ISSN: 2079-0023

This work is devoted to developing of a recommendation system that enables the effective construction of learning trajectories for students studying in universities using learning management systems. The core of the recommendation system will be an artificial deep neural network of forward propagation, which takes as input information about the student and the subject that he or she should study and produces as output the most effective learning trajectory. The neural network is trained on data prepared using multi-agent modeling. The domain was decomposed into separate components and in the process of multi-agent modeling was represented in the form of agents and the environment in which they communicate with each other. The subject of this research is the modeling of the learning process in learning management systems. The purpose of the study is to optimize the student learning process within learning management systems. The subject area was analyzed and studied, the architecture of the recommendation system was developed, the architecture of the multi-agent system was developed, and a mathematical model of agent interaction was developed. To achieve the  goals of the study, it is necessary to solve main tasks, namely: to prepare a training data set using multi-agent modeling and to develop and train a recommendation system that is based on an artificial deep neural network on this data. After completing all the tasks of the work, it is expected that the learning process of students in the learning management system will be optimized in terms of time and resources spent on learning, and the average level of knowledge will be increased.