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

<p>Because of the current relatively lagging writing ability and the current situation of English teaching that separates reading and writing, input and output, this paper proposes the reading and writing integration model to improve English teaching. This paper first introduces the definition of mutual information and interaction information, explains the information theory feature selection method of big data relationship mining technology, and uses the feature selection method to select the features related to the target variable, further derives the hidden structural information between the features, mines the correlation between the target variable and the features, and derives their causal relationship through correlation. Then the specific measures of reading and writing integration are elaborated, including developing students’ sense of language through reading and imitating writing. Finally, the effectiveness of this method is evaluated in terms of learning confidence, achievement and writing level. In terms of English achievement, the mean score after the experiment was 95.44, which was higher than the previous mean score of 82.78. In terms of writing ability, 76.5% of the students said that reading improved the quality of writing, and 65.4% said that the integrated teaching of reading and writing made their writing faster. The integrated teaching mode of reading and writing has improved students’ English proficiency and interest in learning.</p>
</abstract>

Because of the current relatively lagging writing ability and the current situation of English teaching that separates reading and writing, input and output, this paper proposes the reading and writing integration model to improve English teaching. This paper first introduces the definition of mutual information and interaction information, explains the information theory feature selection method of big data relationship mining technology, and uses the feature selection method to select the features related to the target variable, further derives the hidden structural information between the features, mines the correlation between the target variable and the features, and derives their causal relationship through correlation. Then the specific measures of reading and writing integration are elaborated, including developing students’ sense of language through reading and imitating writing. Finally, the effectiveness of this method is evaluated in terms of learning confidence, achievement and writing level. In terms of English achievement, the mean score after the experiment was 95.44, which was higher than the previous mean score of 82.78. In terms of writing ability, 76.5% of the students said that reading improved the quality of writing, and 65.4% said that the integrated teaching of reading and writing made their writing faster. The integrated teaching mode of reading and writing has improved students’ English proficiency and interest in learning.

Research on the intelligent teaching model of integrated English reading and writing classrooms in colleges and universities based on big data technology

Applied Mathematics and Nonlinear Sciences

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

Because of the current relatively lagging writing ability and the current situation of English teaching that separates reading and writing, input and output,...

Research on the intelligent teaching model of integrated English reading and writing classrooms in colleges and universities based on big data technology

Pubblicato online: 04 set 2023

Pagine: -

Ricevuto: 15 nov 2022

Accettato: 15 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00288

Parole chiave
Big data technology, College English, Reading and writing integration, Intelligent teaching, Relationship mining

© 2023 Hui Xu, published by Sciendo

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

Research on the intelligent teaching model of integrated English reading and writing classrooms in colleges and universities based on big data technology

Pubblicato online: 04 set 2023

Pagine: -

Ricevuto: 15 nov 2022

Accettato: 15 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00288

Parole chiaveBig data technology, College English, Reading and writing integration, Intelligent teaching, Relationship mining

© 2023 Hui Xu, published by Sciendo

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

Parole chiave
Big data technology, College English, Reading and writing integration, Intelligent teaching, Relationship mining