Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 397 - 406
Published: Jan. 1, 2025
Language: Английский
Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 397 - 406
Published: Jan. 1, 2025
Language: Английский
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Feb. 19, 2025
In order to solve the problems of inefficient allocation teaching resources and inaccurate recommendation learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining improved random forest algorithm (RFA) based on adaptive enhancement mechanism Graph Neural Network (GNN) algorithm. The public data information such as national intelligent platform are collected, SEOM is trained verified. results show that has high accuracy generalization ability three different scenes: online mixed teaching, personalized project-based teaching. Root Mean Square Error (RMSE) value cross-validation between 0.2 0.5, Absolute (MAE) 0.1 0.5. shows strong stability when dealing with multidimensional educational complex modes. rate remains at 85-97%, indicating its reliability path recommendation. Further analysis chi-square freedom ratio 1.0 2.5, fitting index adjusted both above 0.85, comparative close 0.95, which rationality capturing dependence knowledge points Residual (RMR) Approximation (RMSEA) below 0.05, indicates small residual scene adaptability. addition, abnormal network environment, resource efficiency 60%, Shapley 0.4, can adapt change environment effect still obvious. Generally speaking, optimize recommend effectively improve intelligence decision-making, especially for university administrators technology developers.
Language: Английский
Citations
0Frontiers in Education, Journal Year: 2025, Volume and Issue: 10
Published: Feb. 19, 2025
There is an intense concern in various fields, order to quantify the most complete and explicit way impact that accelerated development of technology basis AI has on education. A very special issue this context represented by teaching methods techniques used teachers. Still, develop refine new based necessary perceptions attitudes toward general its application education become positive people be open experiences using it. The present research explores how different variables like perception towards inclusion Generative tools within materials development, degree familiarity, challenges implementation education, importance process, resilience change can influence perceived utility fostering attitude it usage intention among results are showing exerted above assessed empirical model explain teachers use effectively at levels didactic activity. Implications level human resources management also discussed.
Language: Английский
Citations
0Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)
Published: Feb. 26, 2025
Language: Английский
Citations
0World sustainability series, Journal Year: 2025, Volume and Issue: unknown, P. 357 - 377
Published: Jan. 1, 2025
Language: Английский
Citations
0Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 397 - 406
Published: Jan. 1, 2025
Language: Английский
Citations
0