Factorial, regression and correlation analyses to evaluate the use of neural networks in the university educational process

The subject of research is to evaluate the use of neural networks in the university’s educational process. The purpose of the article is to evaluate the use of ready‒made neural networks in the organization of the educational process using factorial, regression and correlation analyses. The main aspects of the use of neural networks, their impact on student academic performance and the effectiveness of educational programs are considered. The use of ready-made neural networks in the university’s educational process has significant potential to improve learning efficiency. The key factors for successful implementation are the technical equipment of the university, the qualifications of teachers and the availability of ready-made solutions. Regression models have confirmed the positive impact of neural networks on student academic performance, and correlation analysis has revealed a strong link between their use and student motivation. It is recommended to: increase the number of hours allocated to the study of neural networks; conduct regular trainings for teachers.

1. Vieriu A.M., Petrea G. The Impact of Artificial Intelligence (AI) on Students’ Academic Development. Education Sciences. 2025;15(3):343. https://doi.org/10.3390/educsci15030343

2. Vishniakou U.A. Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools. Minsk: BGUIR; 2023. 184 p. (In Russian). Available at: https://libeldoc.bsuir.by/handle/123456789/50731 (accessed: 17.10.2025).

3. Bobrova L.V. Application of factor analysis to assess students’ self-organization. Pedagogy and psychology. Theory and practice. 2023;2:14-17 (In Russian).

4. Vinnik O.G., Grishko T.V. The use of correlation and regression analysis to substantiate the publication activity of the teaching staff as one of the indicators of the quality management system of an institution of higher education. Economy. Business. Finance. 2022;4:8–12 (In Russian). Available at: https://elib.gstu.by/handle/220612/33960 (accessed: 17.10.2025).

5. Hussain S., Gaftandzhieva S., Maniruzzaman M., Doneva R., Muhsin Z.F. Regression analysis of student academic performance using deep learning. Education and Information Technologies. 2021;26:783–798. https://doi.org/10.1007/s10639-020-10241-0

6. Fan G., Liu D., Zhang R., Pan L. The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches. International Journal of STEM Education. 2025;12:16. https://doi.org/10.1186/s40594-025-00537-3