Automated English Teaching System Through Deep Belief Network for Human-Computer Interaction Experience

Abstract

This paper presented the integration of Human-Computer Interaction (HCI) with the Automated Teaching Belief Network (ATBN) to enhance automated English teaching experiences. The proposed ATBN model implemented the Deep Belief Network for the estimation of the factors related to the HCI to promote the experience of the users. The ATBN model uses the deep learning model for the classification in English Teaching. Through the capabilities of deep learning and HCI principles, the ATBN system offers personalized and adaptive learning experiences tailored to individual student needs. The proposed ATBN model estimates the features in English teaching to improve the performance of the Students through HCI model. Simulation analysis expressed that proposed ATBN model improves the pre-test and post-test score by +15 for the English Teaching. The classification values are achieved with accuracy value of 94.8% with minimal loss of 0.12. The assessment of student performance through pre-test and post-test score is improved by 15 for the beginner, intermediate and advanced level. The findings expressed that proposed ATBN model achieves the higher teaching test performance for the HCI language level through the belief network those significantly improves the user experience.