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<p>In this paper, we first explored the mathematical model of reinforcement learning - Markov decision-making process, solved the Bellman optimal solution by strategy iteration and strategy search method, and realized dynamic planning according to the Bellman optimal solution. Then, the students’ satisfaction with practical teaching and the level of students’ vocational ability cultivation were evaluated, and the influence of practical teaching on students’ vocational ability and vocational adaptability was analyzed. Finally, the reform path for practice teaching in colleges and universities was proposed using the reinforcement learning algorithm and impact analysis. The results show that the learning effect of students in the practice teaching environment is 3.8, the learning effect in the ordinary teaching environment is 3.25, and the effect of practice teaching is significant. The correlation coefficient between practice effect and students’ vocational ability is 0.537, which indicates that practice teaching affects students’ vocational ability. This study provides an important reference value for the construction of practical teaching in colleges and universities, as well as for the reform of the college education system.</p>
</abstract>

In this paper, we first explored the mathematical model of reinforcement learning - Markov decision-making process, solved the Bellman optimal solution by strategy iteration and strategy search method, and realized dynamic planning according to the Bellman optimal solution. Then, the students’ satisfaction with practical teaching and the level of students’ vocational ability cultivation were evaluated, and the influence of practical teaching on students’ vocational ability and vocational adaptability was analyzed. Finally, the reform path for practice teaching in colleges and universities was proposed using the reinforcement learning algorithm and impact analysis. The results show that the learning effect of students in the practice teaching environment is 3.8, the learning effect in the ordinary teaching environment is 3.25, and the effect of practice teaching is significant. The correlation coefficient between practice effect and students’ vocational ability is 0.537, which indicates that practice teaching affects students’ vocational ability. This study provides an important reference value for the construction of practical teaching in colleges and universities, as well as for the reform of the college education system.

Research on Reinforcement Learning Algorithm-Based Practical Teaching and Reform Oriented to Enhancement of College Students’ Vocational Ability in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first explored the mathematical model of reinforcement learning - Markov decision-making process, solved the Bellman optimal solution by...

Research on Reinforcement Learning Algorithm-Based Practical Teaching and Reform Oriented to Enhancement of College Students’ Vocational Ability in Colleges and Universities

Data publikacji: 29 lis 2023

Zakres stron: -

Otrzymano: 14 gru 2022

Przyjęty: 09 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01311

Słowa kluczowe
Reinforcement learning algorithms, Markov decision making, Bellman equations, Dynamic programming, Hands-on instruction

© 2023 Yuzi Hu et al., published by Sciendo

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

Research on Reinforcement Learning Algorithm-Based Practical Teaching and Reform Oriented to Enhancement of College Students’ Vocational Ability in Colleges and Universities

Data publikacji: 29 lis 2023

Zakres stron: -

Otrzymano: 14 gru 2022

Przyjęty: 09 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01311

Słowa kluczoweReinforcement learning algorithms, Markov decision making, Bellman equations, Dynamic programming, Hands-on instruction

© 2023 Yuzi Hu et al., published by Sciendo

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

Słowa kluczowe
Reinforcement learning algorithms, Markov decision making, Bellman equations, Dynamic programming, Hands-on instruction