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<p>The purpose of this paper is to investigate the role of critical discourse analysis theory in the context of big data to enlighten university English teaching. To this end, this paper conducts big data mining on university English teaching under the K-nearest neighbor classification algorithm based on the optimization of K-value selection strategy. With the help of the three-dimensional discourse analysis framework under the critical discourse analysis theory, students' evaluation of the current university English in terms of learning value, teaching organization, teaching interaction, teaching coverage, and teacher-student relationship is explored. In terms of the evaluation of learning value, the students' ratings, in descending order, were: inspiring ideas, increasing insight, learning meaningful content, and learning methods. The mean scores were 4.35, 3.81, 3.68, and 3.14, respectively, and for the evaluation of teaching interaction, students thought that the teacher did the best job of encouraging speech, giving an average score of 3.38. This shows that college English teaching should adapt to the development of the times, strengthen students' comprehensive communication skills, and deepen their understanding of English culture.</p>
</abstract>

The purpose of this paper is to investigate the role of critical discourse analysis theory in the context of big data to enlighten university English teaching. To this end, this paper conducts big data mining on university English teaching under the K-nearest neighbor classification algorithm based on the optimization of K-value selection strategy. With the help of the three-dimensional discourse analysis framework under the critical discourse analysis theory, students' evaluation of the current university English in terms of learning value, teaching organization, teaching interaction, teaching coverage, and teacher-student relationship is explored. In terms of the evaluation of learning value, the students' ratings, in descending order, were: inspiring ideas, increasing insight, learning meaningful content, and learning methods. The mean scores were 4.35, 3.81, 3.68, and 3.14, respectively, and for the evaluation of teaching interaction, students thought that the teacher did the best job of encouraging speech, giving an average score of 3.38. This shows that college English teaching should adapt to the development of the times, strengthen students' comprehensive communication skills, and deepen their understanding of English culture.

A Study on the Implications of Critical Discourse Analysis Theory for College English Teaching in the Context of Big Data

Applied Mathematics and Nonlinear Sciences

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

The purpose of this paper is to investigate the role of critical discourse analysis theory in the context of big data to enlighten university English teaching....

A Study on the Implications of Critical Discourse Analysis Theory for College English Teaching in the Context of Big Data

Data publikacji: 28 kwi 2023

Zakres stron: -

Otrzymano: 10 cze 2022

Przyjęty: 21 wrz 2022

DOI: https://doi.org/10.2478/amns.2023.1.00104

Słowa kluczowe
Big data, Critical discourse analysis, University English teaching, K-nearest neighbor algorithm, Learning value

© 2023 Nengchao Pan, published by Sciendo

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

A Study on the Implications of Critical Discourse Analysis Theory for College English Teaching in the Context of Big Data

Data publikacji: 28 kwi 2023

Zakres stron: -

Otrzymano: 10 cze 2022

Przyjęty: 21 wrz 2022

DOI: https://doi.org/10.2478/amns.2023.1.00104

Słowa kluczoweBig data, Critical discourse analysis, University English teaching, K-nearest neighbor algorithm, Learning value

© 2023 Nengchao Pan, published by Sciendo

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

Słowa kluczowe
Big data, Critical discourse analysis, University English teaching, K-nearest neighbor algorithm, Learning value