<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the advancement of the new curriculum reform, English teaching in secondary schools has become increasingly important, and it is especially crucial for establishing a scientific evaluation system for English classroom teaching to adapt to the development of social needs and students’ law of language learning. This study aims to construct the evaluation standard of English classroom teaching in colleges and universities and quantitatively analyze English teaching. First, the English classroom teaching evaluation data are preprocessed by text feature analysis and the data feature weights are calculated. Then, the random forest algorithm was applied to categorize the processed data and construct a hierarchical evaluation analysis matrix. The hierarchical evaluation system was then used to English classroom teaching in colleges and universities to investigate its teaching effectiveness. The results of the study show that the difference between teachers’ and students’ evaluations of the evaluation system is tiny, with a difference of only 0.0149, proving the system’s rationality. Three months after the implementation of the English teaching evaluation system, students’ recognition of the system increased in all four classes, with increases of 0.733, 0.5625, 0.5882, and 0.5. This study provides an effective evaluation method for English classroom teaching in colleges and universities, which can help improve teaching quality and effectiveness.</p>
</abstract>

With the advancement of the new curriculum reform, English teaching in secondary schools has become increasingly important, and it is especially crucial for establishing a scientific evaluation system for English classroom teaching to adapt to the development of social needs and students’ law of language learning. This study aims to construct the evaluation standard of English classroom teaching in colleges and universities and quantitatively analyze English teaching. First, the English classroom teaching evaluation data are preprocessed by text feature analysis and the data feature weights are calculated. Then, the random forest algorithm was applied to categorize the processed data and construct a hierarchical evaluation analysis matrix. The hierarchical evaluation system was then used to English classroom teaching in colleges and universities to investigate its teaching effectiveness. The results of the study show that the difference between teachers’ and students’ evaluations of the evaluation system is tiny, with a difference of only 0.0149, proving the system’s rationality. Three months after the implementation of the English teaching evaluation system, students’ recognition of the system increased in all four classes, with increases of 0.733, 0.5625, 0.5882, and 0.5. This study provides an effective evaluation method for English classroom teaching in colleges and universities, which can help improve teaching quality and effectiveness.

Analysis of English Classroom Teaching Evaluation in Colleges and Universities Based on the Hierarchical Analysis Method

Applied Mathematics and Nonlinear Sciences

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

With the advancement of the new curriculum reform, English teaching in secondary schools has become increasingly important, and it is especially crucial for...

Analysis of English Classroom Teaching Evaluation in Colleges and Universities Based on the Hierarchical Analysis Method

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 15. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0467

Schlüsselwörter
Textual feature analysis, Random forest, Hierarchical evaluation matrix, English teaching evaluation

© 2024 Jingshuai Guo, published by Sciendo

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

Analysis of English Classroom Teaching Evaluation in Colleges and Universities Based on the Hierarchical Analysis Method

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 15. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0467

SchlüsselwörterTextual feature analysis, Random forest, Hierarchical evaluation matrix, English teaching evaluation

© 2024 Jingshuai Guo, published by Sciendo

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

Schlüsselwörter
Textual feature analysis, Random forest, Hierarchical evaluation matrix, English teaching evaluation