<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Education evolves with the times, and innovation in educational philosophy is a prerequisite for efficient teaching. The birth and development of big data have further expanded and deepened the vision of art education, and its application aims not only to understand individual interests and hobbies but, more importantly, to control the learning and development tendencies of the group from the macro context and to provide all-round support for the digitization of art education. This paper proposes a new pedagogical model, ROF-LGB, based on the LightGBM model and the rotating forest. 30 ten-fold hierarchical cross-validation analyses of the four models are then conducted. The ROF-LGB model has nearly 7% more micro-averages and 10% more macro-averages than the other three models. When all datasets were compared, the ROF-LGB model outperformed in both metrics by as much as 65.2% of the datasets and in the comparison of accuracy by 82.6%. Therefore, the ROF-LGB model has greatly improved the accuracy rate based on the rotating forest and LightGBM, making this system a good aid for innovative art education.</p>
</abstract>

Education evolves with the times, and innovation in educational philosophy is a prerequisite for efficient teaching. The birth and development of big data have further expanded and deepened the vision of art education, and its application aims not only to understand individual interests and hobbies but, more importantly, to control the learning and development tendencies of the group from the macro context and to provide all-round support for the digitization of art education. This paper proposes a new pedagogical model, ROF-LGB, based on the LightGBM model and the rotating forest. 30 ten-fold hierarchical cross-validation analyses of the four models are then conducted. The ROF-LGB model has nearly 7% more micro-averages and 10% more macro-averages than the other three models. When all datasets were compared, the ROF-LGB model outperformed in both metrics by as much as 65.2% of the datasets and in the comparison of accuracy by 82.6%. Therefore, the ROF-LGB model has greatly improved the accuracy rate based on the rotating forest and LightGBM, making this system a good aid for innovative art education.

Research on innovative system development of university art education in the era of big data

Applied Mathematics and Nonlinear Sciences

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

Education evolves with the times, and innovation in educational philosophy is a prerequisite for efficient teaching. The birth and development of big data have...

Research on innovative system development of university art education in the era of big data

Data publikacji: 01 sie 2023

Zakres stron: -

Otrzymano: 26 wrz 2022

Przyjęty: 09 lut 2023

DOI: https://doi.org/10.2478/amns.2023.2.00116

Słowa kluczowe
Art Education, Innovation, ROF-LGB model, Rotating forest algorithm, LightGBM model

© 2023 Muchao Zhang, published by Sciendo

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

Research on innovative system development of university art education in the era of big data

Data publikacji: 01 sie 2023

Zakres stron: -

Otrzymano: 26 wrz 2022

Przyjęty: 09 lut 2023

DOI: https://doi.org/10.2478/amns.2023.2.00116

Słowa kluczoweArt Education, Innovation, ROF-LGB model, Rotating forest algorithm, LightGBM model

© 2023 Muchao Zhang, published by Sciendo

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

Słowa kluczowe
Art Education, Innovation, ROF-LGB model, Rotating forest algorithm, LightGBM model