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<p>The swift advancement of digital technology introduces both challenges and opportunities for the education and training of students in intelligent manufacturing programs at higher vocational colleges. This study proposes a digital course model contextualized within the framework of the industrial Internet. To enhance the utilization of digital teaching resources and elevate the quality of these resources, a personalized approach to acquiring teaching resources is developed. Additionally, this paper refines the collaborative filtering recommendation algorithm by integrating similarity metrics and K-means clustering techniques, thereby augmenting the efficiency of learning resource recommendations for professional groups in intelligent manufacturing. And further practice to explore the effects of its digital transformation. Significance at the 0.01 level is shown between the teacher’s concept of teaching and the application of teaching equipment, teaching preparation, learning attitude, learning response, equipment use, teaching resources, and the application effect of digital teaching resources. Digital Transformation Effect = −0.215+0.134*Teacher Concept of Instruction + 0.174*Instructional Equipment Application + 0.134*Instructional Preparation + 0.154*Learning Attitude + 0.14*Learning Response + 0.071*Equipment Usage + 0.045*Instructional Resources. As a result of this study, the construction of a teaching resource platform should be focused on, and the classification, organization, and sequencing of resources should be strengthened.</p>
</abstract>

The swift advancement of digital technology introduces both challenges and opportunities for the education and training of students in intelligent manufacturing programs at higher vocational colleges. This study proposes a digital course model contextualized within the framework of the industrial Internet. To enhance the utilization of digital teaching resources and elevate the quality of these resources, a personalized approach to acquiring teaching resources is developed. Additionally, this paper refines the collaborative filtering recommendation algorithm by integrating similarity metrics and K-means clustering techniques, thereby augmenting the efficiency of learning resource recommendations for professional groups in intelligent manufacturing. And further practice to explore the effects of its digital transformation. Significance at the 0.01 level is shown between the teacher’s concept of teaching and the application of teaching equipment, teaching preparation, learning attitude, learning response, equipment use, teaching resources, and the application effect of digital teaching resources. Digital Transformation Effect = −0.215+0.134*Teacher Concept of Instruction + 0.174*Instructional Equipment Application + 0.134*Instructional Preparation + 0.154*Learning Attitude + 0.14*Learning Response + 0.071*Equipment Usage + 0.045*Instructional Resources. As a result of this study, the construction of a teaching resource platform should be focused on, and the classification, organization, and sequencing of resources should be strengthened.

Research and Practice on Digital Transformation of Higher Vocational Intelligent Manufacturing Professional Group under the Background of Industrial Internet

Department of Computer and Software Engineering

Applied Mathematics and Nonlinear Sciences

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

The swift advancement of digital technology introduces both challenges and opportunities for the education and training of students in intelligent...

Research and Practice on Digital Transformation of Higher Vocational Intelligent Manufacturing Professional Group under the Background of Industrial Internet

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 21 lut 2024

Przyjęty: 17 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1644

Słowa kluczowe
Digitization technology, Cluster analysis, Collaborative filtering technology, Intelligent manufacturing specialty

© 2024 Hong Wang et al., published by Sciendo

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

Research and Practice on Digital Transformation of Higher Vocational Intelligent Manufacturing Professional Group under the Background of Industrial Internet

Data publikacji: 05 lip 2024

Zakres stron: -

Otrzymano: 21 lut 2024

Przyjęty: 17 maj 2024

DOI: https://doi.org/10.2478/amns-2024-1644

Słowa kluczoweDigitization technology, Cluster analysis, Collaborative filtering technology, Intelligent manufacturing specialty

© 2024 Hong Wang et al., published by Sciendo

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

Słowa kluczowe
Digitization technology, Cluster analysis, Collaborative filtering technology, Intelligent manufacturing specialty