Аннотации:
This study explores the application of intelligent modeling to support academic decision-making by integrating a predictive
system into a university’s learning management environment. Utilizing 25,706 records from 16,158 students over an eightyear period (2015–2022), the dataset includes exam results and final grades across 353 subjects within bachelor's, master's,
and PhD programs. After transforming categorical variables - such as education level, course year, and subject name - into
numerical format and applying normalization, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to model
student performance. This system was chosen for its capacity to capture complex, nonlinear relationships while providing
interpretable outputs through fuzzy rules. Comparative evaluation using RMSE, MAE, MSE, and R² metrics demonstrated
that ANFIS consistently outperformed alternative models, achieving the lowest RMSE value of 12.80. These findings
highlight the model’s reliability and its effectiveness in analyzing academic outcomes across diverse student cohorts. By
enabling the early identification of academic risk and delivering interpretable predictions, the system offers practical value
to educational institutions aiming to personalize learning pathways and implement data-informed strategies to enhance
student success in digital learning environments.
