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<p>This paper proposes a digital teaching innovation path for art majors and builds a digital teaching innovation technology framework with digital teaching resource management and online and offline blended teaching as the main modules. In digital teaching resource management, a SCORM digital teaching resource management model is constructed, and a resource recommendation algorithm is designed in combination with a collaborative filtering recommendation algorithm. In blended teaching, a meta-learning recommendation algorithm is utilized to complete the design of a teaching system for CDIO. Finally, experimental classes and control classes were set up to carry out digital teaching practices using the Fine Arts Painting major at University A in Zhejiang Province, China, as the research object. In the analysis of students’ learning behavior performance, the score of students in the experimental class was 4.373 points higher than that of the control class, with a significant difference (P&lt;0.05). The total drawing performance score of the experimental class students was 78.45, and the overall drawing performance was satisfactory. In the analysis of students’ learning performance scale, all the index values of the experimental class were higher than those of the control class. In terms of teachers’ teaching behavior performance, except for teaching monitoring behavior, the experimental class showed significant differences from the control class in all other teaching behavior factors.</p>
</abstract>

This paper proposes a digital teaching innovation path for art majors and builds a digital teaching innovation technology framework with digital teaching resource management and online and offline blended teaching as the main modules. In digital teaching resource management, a SCORM digital teaching resource management model is constructed, and a resource recommendation algorithm is designed in combination with a collaborative filtering recommendation algorithm. In blended teaching, a meta-learning recommendation algorithm is utilized to complete the design of a teaching system for CDIO. Finally, experimental classes and control classes were set up to carry out digital teaching practices using the Fine Arts Painting major at University A in Zhejiang Province, China, as the research object. In the analysis of students’ learning behavior performance, the score of students in the experimental class was 4.373 points higher than that of the control class, with a significant difference (P<0.05). The total drawing performance score of the experimental class students was 78.45, and the overall drawing performance was satisfactory. In the analysis of students’ learning performance scale, all the index values of the experimental class were higher than those of the control class. In terms of teachers’ teaching behavior performance, except for teaching monitoring behavior, the experimental class showed significant differences from the control class in all other teaching behavior factors.

Exploration and Practice of Digital Teaching Innovation Path for Art Majors in the Information Age

Applied Mathematics and Nonlinear Sciences

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

This paper proposes a digital teaching innovation path for art majors and builds a digital teaching innovation technology framework with digital teaching...

Exploration and Practice of Digital Teaching Innovation Path for Art Majors in the Information Age

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 08. März 2024

Akzeptiert: 06. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1573

Schlüsselwörter
SCORM, CDIO, Meta-learning recommendation algorithm, Digital teaching and learning

© 2024 Peng Sun., published by Sciendo

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

Exploration and Practice of Digital Teaching Innovation Path for Art Majors in the Information Age

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 08. März 2024

Akzeptiert: 06. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1573

SchlüsselwörterSCORM, CDIO, Meta-learning recommendation algorithm, Digital teaching and learning

© 2024 Peng Sun., published by Sciendo

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

Schlüsselwörter
SCORM, CDIO, Meta-learning recommendation algorithm, Digital teaching and learning