Технічна інженерія (Jul 2025)
Mathematical model of data collection and processing for a recommendation system for forming an individualized educational program
Abstract
The effectiveness of recommendation systems, which are used in various fields, particularly in the educational system, is a relevant issue for research by scientists and software system developers. Using the example of a system that provides recommendations for selecting educational components of a curriculum based on various parameters, including previous academic achievements of students, data on their past course selections, and the choices of other students, etc. The main goal of the research is to develop a mathematical model capable of effectively predicting grades for courses offered to the student as optional. In the current implementation, ML.NET tools are used, which automate the process of matrix factorization and loss function minimization, simplifying the development and tuning of the model. The main stages of implementation include training the model using the MatrixFactorizationTrainer, defining the trainer's parameters, and optimizing the model through gradient descent and regularization by Frobenius Norm. There are also some directions for improvement, such as integrating content-based filtering. This will allow us to take additional course characteristics, such as type or complexity, into account in the future, significantly improving the accuracy of recommendations. Alternatively, the calculation of similarity between courses or students can be enhanced, which would allow for making recommendations to end users based on similarity. In conclusion, an approach is demonstrated that allows for the creation of an effective recommendation system that can be used to predict student performance and form personalized educational programs. Improving the accuracy of such systems opens up new opportunities for enhancing the educational process, fostering a more individualized approach to learning.
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