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<p>Digital technology, at the center of the fourth technological revolution, is currently transforming all aspects of people’s lives and learning in an unprecedented manner. In light of the ongoing advancements in education technology, this study introduces an enhanced C-ADDIE teaching model, which is competence-oriented and builds on the foundation of the traditional ADDIE model while also catering to the needs of students majoring in digital animation. The enhanced fuzzy clustering algorithm establishes a portrait of the student’s behavior, followed by the introduction of a multi-attention mechanism that builds a digital professional student performance prediction model and a supplementary teaching system. We are developing a teaching model that integrates digital technology with the C-ADDIE model. The testing of the teaching system reveals that the grade prediction accuracy ranges from 88% to 98%, with an average prediction accuracy of 93.02%. The average response time is 1.5ms, the CPU usage remains stable between 5% and 23%, and the overall performance of the teaching system is good. The results of the teaching experiment show that the average grade of the teaching mode of this paper is 5.28 points higher than the average grade of the traditional teaching mode, and at the same time, it also has a significant effect on the improvement of knowledge cultivation, artistic expression and creativity, and artistic aesthetic cultivation of artistic literacy (P&lt;0.05). This study’s results contribute to the transformation of teaching in digital animation and offer a new research idea to enhance students’ artistic literacy.</p>
</abstract>

Digital technology, at the center of the fourth technological revolution, is currently transforming all aspects of people’s lives and learning in an unprecedented manner. In light of the ongoing advancements in education technology, this study introduces an enhanced C-ADDIE teaching model, which is competence-oriented and builds on the foundation of the traditional ADDIE model while also catering to the needs of students majoring in digital animation. The enhanced fuzzy clustering algorithm establishes a portrait of the student’s behavior, followed by the introduction of a multi-attention mechanism that builds a digital professional student performance prediction model and a supplementary teaching system. We are developing a teaching model that integrates digital technology with the C-ADDIE model. The testing of the teaching system reveals that the grade prediction accuracy ranges from 88% to 98%, with an average prediction accuracy of 93.02%. The average response time is 1.5ms, the CPU usage remains stable between 5% and 23%, and the overall performance of the teaching system is good. The results of the teaching experiment show that the average grade of the teaching mode of this paper is 5.28 points higher than the average grade of the traditional teaching mode, and at the same time, it also has a significant effect on the improvement of knowledge cultivation, artistic expression and creativity, and artistic aesthetic cultivation of artistic literacy (P<0.05). This study’s results contribute to the transformation of teaching in digital animation and offer a new research idea to enhance students’ artistic literacy.

Digital Technology-Driven Transformation of Teaching Model of Digital Animation Major and Enhancement of Students’ Artistic Literacy

Applied Mathematics and Nonlinear Sciences

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

Digital technology, at the center of the fourth technological revolution, is currently transforming all aspects of people’s lives and learning in an...

Digital Technology-Driven Transformation of Teaching Model of Digital Animation Major and Enhancement of Students’ Artistic Literacy

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 16 kwi 2024

Przyjęty: 10 lip 2024

DOI: https://doi.org/10.2478/amns-2024-2090

Słowa kluczowe
Maddie teaching model, Fuzzy clustering, Achievement prediction, Digital teaching system, Digital animation major

© 2024 Siyao Li, published by Sciendo

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

Digital Technology-Driven Transformation of Teaching Model of Digital Animation Major and Enhancement of Students’ Artistic Literacy

Data publikacji: 05 sie 2024

Zakres stron: -

Otrzymano: 16 kwi 2024

Przyjęty: 10 lip 2024

DOI: https://doi.org/10.2478/amns-2024-2090

Słowa kluczoweMaddie teaching model, Fuzzy clustering, Achievement prediction, Digital teaching system, Digital animation major

© 2024 Siyao Li, published by Sciendo

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

Słowa kluczowe
Maddie teaching model, Fuzzy clustering, Achievement prediction, Digital teaching system, Digital animation major