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

<p>College English is a very flexible and important subject that needs to be constantly innovated. For the shortcomings of traditional teaching methods, this paper constructs a teaching behavior tree based on reinforcement learning. By analyzing the direction of the behavior tree, using the path planning algorithm to propose better teaching strategies, combined with the <italic>Q</italic> learning algorithm, the teaching theme and the environment of the information are timely updated in the <italic>Q</italic> value table to update the teaching behavior selection strategy, and ultimately obtain the teaching behavior tree. The reinforcement learning algorithm based on <italic>Q </italic>-function interacts with the environment to directly train the method of generating the behavior tree to check whether the teaching behavior tree reaches the optimal standard. Combine reinforcement deep learning with English teaching to introduce a new approach to teaching English. The results show that on the diversified evaluation behavior, teachers of 985 colleges scored significantly higher than teachers of 211 colleges, with a score of 3.05 for teachers of 985 colleges and 2.68 for teachers of 211 colleges. Among the two teaching factors, the F-value of the diversified evaluation is 4.601, and the F-value of the personalized teaching is 3.596, which indicates that both of them have a greater influence on the students and they are able to improve the English proficiency of the students. This study provides a new reference direction for college English teaching, which can effectively improve students’ practical skills.</p>
</abstract>

College English is a very flexible and important subject that needs to be constantly innovated. For the shortcomings of traditional teaching methods, this paper constructs a teaching behavior tree based on reinforcement learning. By analyzing the direction of the behavior tree, using the path planning algorithm to propose better teaching strategies, combined with the Q learning algorithm, the teaching theme and the environment of the information are timely updated in the Q value table to update the teaching behavior selection strategy, and ultimately obtain the teaching behavior tree. The reinforcement learning algorithm based on Q -function interacts with the environment to directly train the method of generating the behavior tree to check whether the teaching behavior tree reaches the optimal standard. Combine reinforcement deep learning with English teaching to introduce a new approach to teaching English. The results show that on the diversified evaluation behavior, teachers of 985 colleges scored significantly higher than teachers of 211 colleges, with a score of 3.05 for teachers of 985 colleges and 2.68 for teachers of 211 colleges. Among the two teaching factors, the F-value of the diversified evaluation is 4.601, and the F-value of the personalized teaching is 3.596, which indicates that both of them have a greater influence on the students and they are able to improve the English proficiency of the students. This study provides a new reference direction for college English teaching, which can effectively improve students’ practical skills.

Research on the Innovative Path of College English Teaching Based on Deep Reinforcement Learning

Applied Mathematics and Nonlinear Sciences

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

College English is a very flexible and important subject that needs to be constantly innovated. For the shortcomings of traditional teaching methods, this...

Research on the Innovative Path of College English Teaching Based on Deep Reinforcement Learning

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 13 sty 2023

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01532

Słowa kluczowe
Deep Reinforcement Learning, Teaching Behavior Tree, Q Learning Algorithm, Path Planning Algorithm, College English Teaching

© 2023 Yumei Zhang et al., published by Sciendo

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

Research on the Innovative Path of College English Teaching Based on Deep Reinforcement Learning

Data publikacji: 18 gru 2023

Zakres stron: -

Otrzymano: 13 sty 2023

Przyjęty: 28 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01532

Słowa kluczoweDeep Reinforcement Learning, Teaching Behavior Tree, Q Learning Algorithm, Path Planning Algorithm, College English Teaching

© 2023 Yumei Zhang et al., published by Sciendo

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

Słowa kluczowe
Deep Reinforcement Learning, Teaching Behavior Tree, Q Learning Algorithm, Path Planning Algorithm, College English Teaching