Application of Multimedia Technology in Teaching English in Colleges and Universities

Abstract

Multimedia plays a crucial role in English teaching by enhancing engagement, providing diverse learning experiences, and catering to different learning styles. English teaching through multimedia, while beneficial, presents challenges. Unequal access to technology and the digital divide can hinder some students' participation. Ensuring digital literacy and quality content selection is crucial to effective use. This paper proposed the Hidden Markov Model for English Teaching (HMM-ET) to improve the performance of college and university students. The proposed HMM-ET model computes the Markov chain of English teaching through multimedia technology. With the implementation of multimedia technology, the HMM model estimates the performance of students in colleges and universities. Through the estimation of HMM-ET the classification of students' performance in English learning is computed with the machine learning model. The performance of the students is examined comparatively with the conventional Support Vector Machine (SVM) and Random Forest. Through analysis of a dataset comprising observation sequences reflecting English learning tasks, HMM-ET consistently outperforms SVM and Random Forest, achieving an average accuracy of 96%, while SVM and Random Forest attain accuracies of 90% and 88% respectively.