Research on the Innovative Path of College English Teaching Based on Deep Reinforcement Learning
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 planni...
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| Vydané v: | Applied mathematics and nonlinear sciences Ročník 9; číslo 1 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Beirut
Sciendo
01.01.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
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| 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
learning algorithm, the teaching theme and the environment of the information are timely updated in the
value table to update the teaching behavior selection strategy, and ultimately obtain the teaching behavior tree. The reinforcement learning algorithm based on
-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. |
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| AbstractList | 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. 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. 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 learning algorithm, the teaching theme and the environment of the information are timely updated in the value table to update the teaching behavior selection strategy, and ultimately obtain the teaching behavior tree. The reinforcement learning algorithm based on -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. |
| Author | Zhang, Yumei Cheng, Hongmei |
| Author_xml | – sequence: 1 givenname: Yumei surname: Zhang fullname: Zhang, Yumei organization: School of Humanities and Law, Hebei University of Engineering, Handan, Hebei, 056001, China – sequence: 2 givenname: Hongmei surname: Cheng fullname: Cheng, Hongmei email: hmchenghc@163.com organization: School of Humanities and Law, Hebei University of Engineering, Handan, Hebei, 056001, China |
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| Cites_doi | 10.1504/IJGUC.2022.124394 10.1093/elt/ccw006 10.1111/bjet.12361 10.1016/j.system.2014.09.019 10.3390/su14010493 10.1177/1362168820979855 10.1017/S0266078415000267 10.1155/2021/8993173 10.1155/2022/7265254 10.3991/ijet.v14i12.10810 10.1016/j.ipm.2021.102540 10.3390/su14127130 10.1177/1362168820931636 10.1002/tesq.138 |
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| SubjectTerms | 97C70 Algorithms College English Teaching Colleges & universities Deep Reinforcement Learning Learning Path Planning Algorithm Q Learning Algorithm Teaching Behavior Tree |
| Title | Research on the Innovative Path of College English Teaching Based on Deep Reinforcement Learning |
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