Integration of neuro-fuzzy modeling in learning management systems to predict academic achievement of graduates
Artykuł w czasopiśmie
MNiSW
20
Lista 2024
| Status: | |
| Autorzy: | Zulkhazhav Altanbek, Miłosz Marek, Nazyrova Aizhan, Barlybayev Alibek, Bekmanova Gulmira |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 8 |
| Strony: | 4900 - 4914 |
| Efekt badań statutowych | NIE |
| Finansowanie: | This research was funded by the Scientific Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant Number: AP19679847). |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 5 czerwca 2025 |
| Abstrakty: | angielski |
| 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 eight-year 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. |
