Optimized - Back Propagation approach for Evaluating the Chinese Teaching Performance

By accelerating the global combination, the demands from English instruction are growing continuously. The struggle of Chinese English learners for learning the spoken English because of restricted environment in English learning and conditions of teaching in China. The growth of Artificial Intelligence (AI) technique and language teaching and learning methods has new method language teaching and learning. The Optimized - Back Propagation (O-BP) algorithm is developed for Chinese teaching performance evaluation. This approach maintains the highest margin of separation among origin and whole training data samples. The Chinese data are recorded through CoolEdit and it is pre-processed to improve the quality of data. Then, the meaningful features are extracted and it is evaluated by using the O-BP approach. The developed O-BP approach attained 0.3 on 100 nodes, 0.4 on 200 nodes, 0.5 on 300 nodes, 0.6 on 400 nodes and 0.7 on 500 nodes of convergence speed. The developed O-BP approach attained the 0.9543 accuracy while comparing with existing techniques while compared to existing techniques like Back Propagation (BP) approach.