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<p>With the development of society, the artistic cultivation requirements for talents are increasing. This paper discusses in depth the main problems in the current reform of art teaching and puts forward targeted solution strategies, aiming to improve the overall quality of art teaching. In the study, the key factors affecting the quality of art teaching in colleges and universities were analyzed using the VAR model, and a multimodal art education knowledge map was constructed using the Viterbi method. Through the ALKPT knowledge tracking model, this paper assessed and scored students’ mastery of art knowledge and incorporated the results into the art classroom quality evaluation module. Based on the scoring data, the art classroom quality features were standardized, and the comprehensive evaluation results were derived through feature fusion dynamic weight assignment. Impulse response analysis showed that the number of returnees who studied abroad to learn about art positively impacted art teaching. The empirical results showed that after the reform of art teaching, the students’ art theory scores increased by 5 points on average, the scores were distributed between 10 and 20 points, and the achievement gap between students was reduced. This study provides a robust strategy and evaluation tool for optimizing art teaching, improving teaching quality and promoting students’ artistic literacy.</p>
</abstract>

With the development of society, the artistic cultivation requirements for talents are increasing. This paper discusses in depth the main problems in the current reform of art teaching and puts forward targeted solution strategies, aiming to improve the overall quality of art teaching. In the study, the key factors affecting the quality of art teaching in colleges and universities were analyzed using the VAR model, and a multimodal art education knowledge map was constructed using the Viterbi method. Through the ALKPT knowledge tracking model, this paper assessed and scored students’ mastery of art knowledge and incorporated the results into the art classroom quality evaluation module. Based on the scoring data, the art classroom quality features were standardized, and the comprehensive evaluation results were derived through feature fusion dynamic weight assignment. Impulse response analysis showed that the number of returnees who studied abroad to learn about art positively impacted art teaching. The empirical results showed that after the reform of art teaching, the students’ art theory scores increased by 5 points on average, the scores were distributed between 10 and 20 points, and the achievement gap between students was reduced. This study provides a robust strategy and evaluation tool for optimizing art teaching, improving teaching quality and promoting students’ artistic literacy.

Analysis of Outstanding Problems and Countermeasures in the Reform of Fine Arts Teaching in China’s Colleges and Universities Based on the VAR Model

Shanxi Normal University Linfen College Arts Department

Applied Mathematics and Nonlinear Sciences

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

With the development of society, the artistic cultivation requirements for talents are increasing. This paper discusses in depth the main problems in the...

Analysis of Outstanding Problems and Countermeasures in the Reform of Fine Arts Teaching in China’s Colleges and Universities Based on the VAR Model

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 17. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0465

Schlüsselwörter
VAR model, ALKPT knowledge tracking, Feature fusion, Knowledge mapping, Art teaching reform

© 2024 Feifei Duan, published by Sciendo

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

Analysis of Outstanding Problems and Countermeasures in the Reform of Fine Arts Teaching in China’s Colleges and Universities Based on the VAR Model

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 10. Jan. 2024

Akzeptiert: 17. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0465

SchlüsselwörterVAR model, ALKPT knowledge tracking, Feature fusion, Knowledge mapping, Art teaching reform

© 2024 Feifei Duan, published by Sciendo

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

Schlüsselwörter
VAR model, ALKPT knowledge tracking, Feature fusion, Knowledge mapping, Art teaching reform