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

<p>As we enter the information technology era of the 21st century, universities are the main place for talent cultivation, but as an important part of higher Education, they are not sufficiently aware of innovative talent cultivation, and their own development is immature, which leads to the unsound innovative talent cultivation model. Therefore, in this paper, we use the Markov model-based test case generation technique to explore in depth the techniques of transfer probability acquisition, random selection, and adequacy determination and improve the Markov model with linear regression to further strengthen the robustness of the prediction algorithm, reduce the error of prediction and improve the accuracy of prediction. Through the processing of student data, the results show that the standard deviation of the improved Markov model is 0.06903 and the processing time is 87 seconds, the standard deviation of the original model is 0.07255 and the processing time is 133 seconds, and the larger the standard deviation of the experimental sample the less accurate the experiment is. The improved Markov model has a good experimental effect with little error, which can effectively optimize the training mode of innovative talents in universities.</p>
</abstract>

As we enter the information technology era of the 21st century, universities are the main place for talent cultivation, but as an important part of higher Education, they are not sufficiently aware of innovative talent cultivation, and their own development is immature, which leads to the unsound innovative talent cultivation model. Therefore, in this paper, we use the Markov model-based test case generation technique to explore in depth the techniques of transfer probability acquisition, random selection, and adequacy determination and improve the Markov model with linear regression to further strengthen the robustness of the prediction algorithm, reduce the error of prediction and improve the accuracy of prediction. Through the processing of student data, the results show that the standard deviation of the improved Markov model is 0.06903 and the processing time is 87 seconds, the standard deviation of the original model is 0.07255 and the processing time is 133 seconds, and the larger the standard deviation of the experimental sample the less accurate the experiment is. The improved Markov model has a good experimental effect with little error, which can effectively optimize the training mode of innovative talents in universities.

The optimization path of core literacy of innovative talents in universities based on the improved Markov model

Chongqing University of Posts and Telecommunications

Applied Mathematics and Nonlinear Sciences

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

As we enter the information technology era of the 21st century, universities are the main place for talent cultivation, but as an important part of higher...

The optimization path of core literacy of innovative talents in universities based on the improved Markov model

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 12 avr. 2022

Accepté: 28 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00118

Mots clés
Markov model, Innovative talents, Training model, Linear regression, Institutional transformation

© 2023 Zhengli Zhang, published by Sciendo

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

The optimization path of core literacy of innovative talents in universities based on the improved Markov model

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 12 avr. 2022

Accepté: 28 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00118

Mots clésMarkov model, Innovative talents, Training model, Linear regression, Institutional transformation

© 2023 Zhengli Zhang, published by Sciendo

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

Mots clés
Markov model, Innovative talents, Training model, Linear regression, Institutional transformation