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<p>In this study, the ability level of art and design students in colleges and universities was accurately calculated by adopting the Logistics model in item response theory, combined with learner-question response data, and the maximum likelihood estimation method. Further, the study constructs a personalized recommendation model based on students’ competence, which utilizes the singular value decomposition technique to calculate students’ similarity coefficients, and achieves accurate personalized learning path recommendation. Through descriptive analysis, this paper delves into the specific application and effect of this personalized recommendation algorithm in the teaching reform of art and design majors in colleges and universities. The simulation analysis method evaluates the impact of online art and design professional teaching. The results show that this algorithm performs best in scoring prediction accuracy when the regularization coefficient is set to 0.02, and its RMSE value reaches 0.95012.This finding confirms that the personalized recommendation algorithm based on the students’ ability can effectively promote the reform of art and design teaching in colleges and universities, and realize tailored teaching. In addition, through accurate learning path recommendation, the algorithm significantly improves students’ learning ability and enthusiasm and provides valuable guidance and reference for teaching reform in colleges and universities.</p>
</abstract>

In this study, the ability level of art and design students in colleges and universities was accurately calculated by adopting the Logistics model in item response theory, combined with learner-question response data, and the maximum likelihood estimation method. Further, the study constructs a personalized recommendation model based on students’ competence, which utilizes the singular value decomposition technique to calculate students’ similarity coefficients, and achieves accurate personalized learning path recommendation. Through descriptive analysis, this paper delves into the specific application and effect of this personalized recommendation algorithm in the teaching reform of art and design majors in colleges and universities. The simulation analysis method evaluates the impact of online art and design professional teaching. The results show that this algorithm performs best in scoring prediction accuracy when the regularization coefficient is set to 0.02, and its RMSE value reaches 0.95012.This finding confirms that the personalized recommendation algorithm based on the students’ ability can effectively promote the reform of art and design teaching in colleges and universities, and realize tailored teaching. In addition, through accurate learning path recommendation, the algorithm significantly improves students’ learning ability and enthusiasm and provides valuable guidance and reference for teaching reform in colleges and universities.

Research on the Teaching Reform Path of Art and Design Specialties in Colleges and Universities in the Era of Artificial Intelligence

Applied Mathematics and Nonlinear Sciences

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

In this study, the ability level of art and design students in colleges and universities was accurately calculated by adopting the Logistics model in item...

Research on the Teaching Reform Path of Art and Design Specialties in Colleges and Universities in the Era of Artificial Intelligence

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 04 gen 2024

Accettato: 14 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0391

Parole chiave
Personalized recommendation, Similarity coefficient, Maximum likelihood estimation, Item response theory, Art design profession

© 2024 Jie Wen, published by Sciendo

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

Research on the Teaching Reform Path of Art and Design Specialties in Colleges and Universities in the Era of Artificial Intelligence

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 04 gen 2024

Accettato: 14 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0391

Parole chiavePersonalized recommendation, Similarity coefficient, Maximum likelihood estimation, Item response theory, Art design profession

© 2024 Jie Wen, published by Sciendo

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

Parole chiave
Personalized recommendation, Similarity coefficient, Maximum likelihood estimation, Item response theory, Art design profession