Optimization of neural network models for personalization of educational processes: comparison of methods and variability analysis of architectures

Modern learning systems are often faced with the need to account for non-linear relationships between the parameters of the learning process, individual characteristics of students and final results. However, the use of traditional statistical methods (e.g., linear regression) is limited by their ability to deal with uncertainty and complex data. An additional difficulty is the lack of systematized approaches to selecting the optimal neural network architecture, which makes it difficult to implement them in educational practice. The aim of the study is comparison of neural network models and traditional machine learning methods (multinomial logistic regression, random forests) for predicting learning outcomes, and determination of the optimal parameters of neural network architecture using the Python programming language. The paper presents the performance results of neural networks, multinomial regression and ensemble methods (random forest, XGBoost) on educational process data. The influence of the number of neurons in the hidden layer on the accuracy of the model has been analyzed. The universality of neural network models as approximators for predicting learning outcomes taking into account individual characteristics of students has been confirmed. The results of the study can be used to develop adaptive educational platforms that provide personalized selection of assignments and dynamic adjustment of curricula, to create the training data analysis systems integrating neural network and fuzzy models for working with uncertain pedagogical parameters, to optimize the knowledge assessment processes through the choice of optimal neural network architecture taking into account the available resources.