The evaluation of course teaching effect based on improved RBF neural network

• Published in: Systems and soft computing 2024-12, Vol.6, p.200085, Article 200085
• Main Authors: Wu, Hanmei, Cai, Xiaoqing, Feng, Man
• Format: Article
• Language: English
• Subjects: Online education; RBF neural network; Teacher-student evaluation; Teaching effect
• ISSN: 2772-9419; 2772-9419
• DOI: 10.1016/j.sasc.2024.200085

As basic education is increasingly digitized, the need for better teaching and learning quality also rises. Teaching reform is crucial to achieve this, and incorporating the Levenberg-Marquardt (L-M) into the Radial Basis Function (RBF) can help establish a fair online teaching evaluation system. The experimental results showed that the convergence ability of the model was significantly improved compared with the traditional RBF neural network. The overall mean square error of the improved model was 10°. The actual value prediction accuracy of the improved model is higher than that of the Backpropagation (BP). When the actual value was at its peak, the accuracy reached 98 %, the overall fluctuation range of absolute error was low, the highest absolute error value reached 0.78, and the average absolute error was below 0.5. With targeted improvements, teachers and students could better understand and change their own learning situations, as reflected in empirical evaluations.