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

<p>This paper proposes an interactive study of online practice teaching based on LightGBM model. To avoid the overfitting of training data by LightGBM model, the objective function of LightGBM model is derived utilizing GBDT gradient boosting tree to optimize the overfitting problem of training data. Based on the interactive study of online practice teaching based on the LightGBM model, the construction of the accounting practice teaching system is completed by using B/S mode, and the simulation analysis of online practice teaching of accounting majors in the context of the artificial intelligence era is carried out. The results show that the students in class C, with an AUC value of 0.819, are higher than that before optimization by 0.095, and similarly comparing the AUC values of other classes are higher than that before optimization. The LightGBM model optimized by the grid search algorithm can effectively identify and interact with students’ accounting practice behavioral characteristics, and to a certain extent, effectively predict students’ accounting practice ability. This study has the potential to guide students in mastering accounting practice knowledge, guaranteeing quality practice teaching, and fostering the growth of accounting professionals.</p>
</abstract>

This paper proposes an interactive study of online practice teaching based on LightGBM model. To avoid the overfitting of training data by LightGBM model, the objective function of LightGBM model is derived utilizing GBDT gradient boosting tree to optimize the overfitting problem of training data. Based on the interactive study of online practice teaching based on the LightGBM model, the construction of the accounting practice teaching system is completed by using B/S mode, and the simulation analysis of online practice teaching of accounting majors in the context of the artificial intelligence era is carried out. The results show that the students in class C, with an AUC value of 0.819, are higher than that before optimization by 0.095, and similarly comparing the AUC values of other classes are higher than that before optimization. The LightGBM model optimized by the grid search algorithm can effectively identify and interact with students’ accounting practice behavioral characteristics, and to a certain extent, effectively predict students’ accounting practice ability. This study has the potential to guide students in mastering accounting practice knowledge, guaranteeing quality practice teaching, and fostering the growth of accounting professionals.

Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors

Applied Mathematics and Nonlinear Sciences

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

This paper proposes an interactive study of online practice teaching based on LightGBM model. To avoid the overfitting of training data by LightGBM model, the...

Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors

Published Online: Jan 31, 2024

Page range: -

Received: Dec 22, 2023

Accepted: Dec 28, 2023

DOI: https://doi.org/10.2478/amns-2024-0272

Keywords
LightGBM model, GBDT, Objective function, Practice teaching system, Accounting specialty

© 2024 Jian Wu et al., published by Sciendo

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

Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors

Published Online: Jan 31, 2024

Page range: -

Received: Dec 22, 2023

Accepted: Dec 28, 2023

DOI: https://doi.org/10.2478/amns-2024-0272

KeywordsLightGBM model, GBDT, Objective function, Practice teaching system, Accounting specialty

© 2024 Jian Wu et al., published by Sciendo

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

Keywords
LightGBM model, GBDT, Objective function, Practice teaching system, Accounting specialty