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<p>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.</p>
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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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

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

Online veröffentlicht: 28. Aug. 2023

Seitenbereich: -

Eingereicht: 18. Nov. 2022

Akzeptiert: 08. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00250

Schlüsselwörter
Big data technology, Higher education English, Sequencing model, Personalized teaching system, Data feature

© 2023 Lilin Ren, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Online veröffentlicht: 28. Aug. 2023

Seitenbereich: -

Eingereicht: 18. Nov. 2022

Akzeptiert: 08. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00250

SchlüsselwörterBig data technology, Higher education English, Sequencing model, Personalized teaching system, Data feature

© 2023 Lilin Ren, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data technology, Higher education English, Sequencing model, Personalized teaching system, Data feature