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<p>In the dynamic planning of reinforcement learning algorithms based on inverse recursion, this research shows how well Q-learning performs in finding the optimal decision function for accurate English teaching intervention in colleges and universities using data simulation. This simultaneously contrasts the two scenarios of calculating the best decision function independently and in a single stage using the Q-learning method. The correlation analysis between the innovative English teaching model and learners’ learning effectiveness was carried out by demonstrating the great clusters of the subset of English teaching data in colleges and universities. The results show that the correlation coefficients of English innovative teaching mode and learning outcomes are 0.385 and 0.276, respectively, in which H2-P1 and H2-P2 involve the structure of students’ personalized learning of English, whether it is an edge or a node, and the learning behaviors are not completely autonomous processes. This study proposes innovative strategies for improving the quality of university English teaching and laying a good educational foundation for students’ English learning.</p>
</abstract>

In the dynamic planning of reinforcement learning algorithms based on inverse recursion, this research shows how well Q-learning performs in finding the optimal decision function for accurate English teaching intervention in colleges and universities using data simulation. This simultaneously contrasts the two scenarios of calculating the best decision function independently and in a single stage using the Q-learning method. The correlation analysis between the innovative English teaching model and learners’ learning effectiveness was carried out by demonstrating the great clusters of the subset of English teaching data in colleges and universities. The results show that the correlation coefficients of English innovative teaching mode and learning outcomes are 0.385 and 0.276, respectively, in which H2-P1 and H2-P2 involve the structure of students’ personalized learning of English, whether it is an edge or a node, and the learning behaviors are not completely autonomous processes. This study proposes innovative strategies for improving the quality of university English teaching and laying a good educational foundation for students’ English learning.

The Application of Big Data Technology in University English Teaching Mode Innovation and Effect Analysis

Applied Mathematics and Nonlinear Sciences

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

In the dynamic planning of reinforcement learning algorithms based on inverse recursion, this research shows how well Q-learning performs in finding the...

The Application of Big Data Technology in University English Teaching Mode Innovation and Effect Analysis

Data publikacji: 11 gru 2023

Zakres stron: -

Otrzymano: 23 mar 2023

Przyjęty: 21 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01453

Słowa kluczowe
Data simulation, Reinforcement learning algorithm, Backward recursion, Q-learning, Optimal decision function

© 2023 Xiaoxia Hao, published by Sciendo

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

The Application of Big Data Technology in University English Teaching Mode Innovation and Effect Analysis

Data publikacji: 11 gru 2023

Zakres stron: -

Otrzymano: 23 mar 2023

Przyjęty: 21 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01453

Słowa kluczoweData simulation, Reinforcement learning algorithm, Backward recursion, Q-learning, Optimal decision function

© 2023 Xiaoxia Hao, published by Sciendo

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

Słowa kluczowe
Data simulation, Reinforcement learning algorithm, Backward recursion, Q-learning, Optimal decision function