Research on the Optimization Method of Project-Based Learning Design for Chinese Teaching Based on Interference-Tolerant Fast Convergence Zeroing Neural Network

• Published in: International journal of computational intelligence systems Vol. 17; no. 1; pp. 1 - 14
• Main Authors: Bai, Weihua, Geng, Guoli, Fu, Xuan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 04.07.2024; Springer
• Subjects: Artificial Intelligence; Battle royal optimization; Computational Intelligence; Control; Engineering; Mathematical Logic and Foundations; Mechatronics; Project-based learning; Research Article; Robotics; Unscented trainable Kalman filter; Unscented trainable Kalman filter; Battle royal optimization; Project-based learning
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-024-00532-6

The system known as project-based learning, which is applied to specific courses without compromising the coverage of the necessary technical material, uses projects to drive knowledge. The plan and implementation of project-based learning in Chinese teaching as a major project, which embraces undergraduate creativity and places an emphasis on real-world, open-ended projects are discussed in this paper. In this paper, research on optimization method of project-based learning design for Chinese teaching based on optimized interference-tolerant fast convergence zeroing neural network (PBLD-ITFCZNN-BRO). It consists of three stages, import phase, main stage and evaluation stage. In the initial phase, the teacher separated the students to groups before the lecture to make sure that every group poses various traits, with some strong leadership skills and hands-on skills. The second phase of the PBL procedure helped transform what is primarily a passive learning environment (taking notes, listening, and sitting) into a more dynamic, student-centered, and interactive one. Students presented data, articulated their concepts, and then optimized their approaches to problem-solving during the evaluation step. The teachers concluded by summarizing. The performance of the proposed PBLD-ITFCZNN-BRO approach contains 15.26%, 20.42% and 21.27% greater accuracy, and 15.61%, 17.50% and 20.24% greater precision rate, compared with Investigation of Computer Network Technology on New Media Problem-Basis Learning Teaching Mode (CNT-PBLTM), PBL Model Basis application on Deep Learning in Physical Education Classroom Integrating Production with Education (PBL-DL-PEC), Interdisciplinary project-based learning: experiences with reflections from teaching electronic engineering at china (PBL-EEC) techniques, respectively.