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

<p>With the rapid development of information technology, digital technology is also continuously applied to the teaching of higher education, and colleges and universities more and more emphasize the application of intelligent teaching guidance systems. A feature-weighted Bayesian algorithm is constructed in this study by assigning different weights to each attribute depending on its influence on the Bayesian classification results. On this basis, combined with the error-reversing BP neural network algorithm, it realizes refined intelligent instruction and develops an intelligent instruction system for higher education based on the ternary model. The transaction success rate reaches 98% when the number of concurrent users is below 4000, and the transaction success rate is 90% at 8000, and the system has good stability. The average classification accuracy of FWNB algorithm in this paper is 84.95%, which is larger than SWNB and HCWNB, and the average classification time is 3.17s, which is smaller than SWNB and HCWNB. The FWNB classifier achieves high classification accuracy at a low computational cost. The system in this paper can better mobilize learners’ enthusiasm, drive learners to study diligently and inspire learners to improve their learning ability, which leads to improved teaching quality.</p>
</abstract>

With the rapid development of information technology, digital technology is also continuously applied to the teaching of higher education, and colleges and universities more and more emphasize the application of intelligent teaching guidance systems. A feature-weighted Bayesian algorithm is constructed in this study by assigning different weights to each attribute depending on its influence on the Bayesian classification results. On this basis, combined with the error-reversing BP neural network algorithm, it realizes refined intelligent instruction and develops an intelligent instruction system for higher education based on the ternary model. The transaction success rate reaches 98% when the number of concurrent users is below 4000, and the transaction success rate is 90% at 8000, and the system has good stability. The average classification accuracy of FWNB algorithm in this paper is 84.95%, which is larger than SWNB and HCWNB, and the average classification time is 3.17s, which is smaller than SWNB and HCWNB. The FWNB classifier achieves high classification accuracy at a low computational cost. The system in this paper can better mobilize learners’ enthusiasm, drive learners to study diligently and inspire learners to improve their learning ability, which leads to improved teaching quality.

Research on the method of fine-tuning teaching quality improvement in higher education under digital empowerment

Technical College of Posts and Telecommunications

Applied Mathematics and Nonlinear Sciences

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

With the rapid development of information technology, digital technology is also continuously applied to the teaching of higher education, and colleges and...

Research on the method of fine-tuning teaching quality improvement in higher education under digital empowerment

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 25 lut 2024

Przyjęty: 22 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1734

Słowa kluczowe
Digital empowerment, Feature-weighted Bayesian, BP neural network, Intelligent granting guidance system, Education teaching quality

© 2024 Xiaotian Xia., published by Sciendo

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

Research on the method of fine-tuning teaching quality improvement in higher education under digital empowerment

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 25 lut 2024

Przyjęty: 22 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1734

Słowa kluczoweDigital empowerment, Feature-weighted Bayesian, BP neural network, Intelligent granting guidance system, Education teaching quality

© 2024 Xiaotian Xia., published by Sciendo

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

Słowa kluczowe
Digital empowerment, Feature-weighted Bayesian, BP neural network, Intelligent granting guidance system, Education teaching quality