English Online Teaching System using Chaotic Zebra Optimization Algorithm and Long Short-Term Memory Based on the Background of Industry Education Integration

The rise of English as a global language has led to a significant in English language online teaching system. In the context of the integration of industry and education, it is necessary to use advanced technology to improve the quality of English teaching. The imbalance of power between different social groups negatively impact learner’s confidence and sense of identity. The struggle to convey the information or miscommunication occurred, native spacer was unexpected issue then affect the performance. This paper, proposed Chaotic Zebra Optimization Algorithm and Long Short-Term Memory (CZOA-LSTM) technique efficiently capture the interaction session and achieve better accuracy. The CZOA integrating the system of learning materials and ensures content, select better solution. This approach helps to dynamically selecting the best teaching strategies, based on student performance and lead to balance the feature. The LSTM technique helps to providing a more personalized learning experience by analysing past behaviours such as the student strengths and suggesting practice area where student needs to improvement. The pre-processing stage using min-max technique is scale the feature and uniformly performed the data. The proposed CZOA-LSTM technique achieve better accuracy of 98%, precision of 97%, recall of 96%, f1-score of 97% respectively compared to existing method such as Generation Algorithm Network (GAN) and Artificial Intelligence (AI) approaches.