Exploring Innovative Strategies for Teaching English in Higher Education with the Support of Big Data

Abstract

Abstract Under the background of the rapid development of big data technology, how to use big data technology to improve the quality and efficiency of English teaching in higher education is currently a hot topic of key concern in society and education circles. This paper is firstly based on the feedback data of English teaching from students and teachers in higher education institutions. The ranking model of fused similarity pre-processes the feedback data to get the data to feature values of English-level similarity. Then the personalized teaching system is constructed based on the data feature values, and the overall teaching framework and ideas of the MOOC personalized teaching system are proposed. The satisfaction of the MOOC personalized teaching system platform in the past five years, from 2018-2022, was analyzed employing a questionnaire, and the results showed that: on the teachers’ side, the overall satisfaction increased from the initial 53.46% to 73.64% year by year. Regarding parents, the overall satisfaction rate increased from 46.72% at the beginning to 65.76% year by year. In terms of students, the satisfaction rate increased from 75.37% at the beginning to 91.23% year by year. Overall, students showed better satisfaction with the English personalized instruction system platform than parents and teachers. This study can lead to pedagogical innovations based on the data feedback on students’ English performance, which can improve teachers’ teaching ability and pedagogical innovations.