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<p>English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet the diverse learning needs of students. Big data and decision tree technology provide a new way for teaching content optimization, which can achieve a high degree of matching between educational resources and students’ needs, and improve teaching effectiveness and learning efficiency. This study explores the application of decision trees and big data technology in optimizing English teaching content in colleges and universities. By constructing an ontology tree model of learning resources and adopting similarity calculation and personalized recommendation algorithms, the study achieves personalized learning resource recommendations for students. The study utilizes comparative experimental methods to analyze achievement changes between the experimental and control classes after customized learning. The personalized teaching program’s effectiveness can be confirmed by the fact that the average final grade of students in the experimental class is 1.5 points higher than that of the control class. The Analysis of CET-4 scores found that personalized teaching improved students’ English listening, reading, and writing skills. This paper combines decision trees and big data technology to optimize teaching content, effectively enhancing students’ English learning outcomes and providing new teaching strategies for English education in colleges and universities.</p>
</abstract>

English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet the diverse learning needs of students. Big data and decision tree technology provide a new way for teaching content optimization, which can achieve a high degree of matching between educational resources and students’ needs, and improve teaching effectiveness and learning efficiency. This study explores the application of decision trees and big data technology in optimizing English teaching content in colleges and universities. By constructing an ontology tree model of learning resources and adopting similarity calculation and personalized recommendation algorithms, the study achieves personalized learning resource recommendations for students. The study utilizes comparative experimental methods to analyze achievement changes between the experimental and control classes after customized learning. The personalized teaching program’s effectiveness can be confirmed by the fact that the average final grade of students in the experimental class is 1.5 points higher than that of the control class. The Analysis of CET-4 scores found that personalized teaching improved students’ English listening, reading, and writing skills. This paper combines decision trees and big data technology to optimize teaching content, effectively enhancing students’ English learning outcomes and providing new teaching strategies for English education in colleges and universities.

Research on the Optimization of English Teaching Content in Colleges and Universities by Combining Decision Tree and Big Data Technology

Applied Mathematics and Nonlinear Sciences

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

English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet...

Research on the Optimization of English Teaching Content in Colleges and Universities by Combining Decision Tree and Big Data Technology

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 11 apr 2024

Accettato: 17 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0858

Parole chiave
Big Data, Personalized Instruction, Decision Trees, Learning Resource Ontology Trees

© 2024 Huimin Cheng et al., published by Sciendo

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

Research on the Optimization of English Teaching Content in Colleges and Universities by Combining Decision Tree and Big Data Technology

Pubblicato online: 03 mag 2024

Pagine: -

Ricevuto: 11 apr 2024

Accettato: 17 apr 2024

DOI: https://doi.org/10.2478/amns-2024-0858

Parole chiaveBig Data, Personalized Instruction, Decision Trees, Learning Resource Ontology Trees

© 2024 Huimin Cheng et al., published by Sciendo

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

Parole chiave
Big Data, Personalized Instruction, Decision Trees, Learning Resource Ontology Trees