Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors
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. B...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0272 |
| _version_ | 1797303101414178816 |
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| author | Wu Jian Chen Xuemei Zhang Yuan Wang Hui |
| author_facet | Wu Jian Chen Xuemei Zhang Yuan Wang Hui |
| author_sort | Wu Jian |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T23:48:06Z |
| format | Article |
| id | doaj.art-5e29bbb646dd45b1a56e972ad034bc47 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T23:48:06Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-5e29bbb646dd45b1a56e972ad034bc472024-02-19T09:03:36ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0272Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting MajorsWu Jian0Chen Xuemei1Zhang Yuan2Wang Hui31Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 052161, China.1Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 052161, China.1Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 052161, China.1Shijiazhuang Information Engineering Vocational College, Shijiazhuang, Hebei, 052161, China.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.https://doi.org/10.2478/amns-2024-0272lightgbm modelgbdtobjective functionpractice teaching systemaccounting specialty68t01 |
| spellingShingle | Wu Jian Chen Xuemei Zhang Yuan Wang Hui Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors Applied Mathematics and Nonlinear Sciences lightgbm model gbdt objective function practice teaching system accounting specialty 68t01 |
| title | Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors |
| title_full | Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors |
| title_fullStr | Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors |
| title_full_unstemmed | Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors |
| title_short | Artificial Intelligence Technology Empowers Practical Teaching of Higher Vocational Accounting Majors |
| title_sort | artificial intelligence technology empowers practical teaching of higher vocational accounting majors |
| topic | lightgbm model gbdt objective function practice teaching system accounting specialty 68t01 |
| url | https://doi.org/10.2478/amns-2024-0272 |
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