Research on the Strategy of Enhancing Practice Teaching Effectiveness in Professorship Education Through CAD and Neural Network Technology

This study explored the integration of computer-aided design (CAD) and neural network technology in vocational education, focusing on course design, skill training, and project-based learning. Through analysis, the study reveals how this technological integration improved students' practical skills, innovative thinking, and problem-solving abilities. Feedback from students indicated high satisfaction with the personalized resources provided by the system, which effectively met their learning needs. Empirical findings showed significant improvements in students' practical capabilities after applying CAD and neural network technology. Compared to traditional models, students demonstrated faster execution and better reporting skills. This integration marks a breakthrough in practical teaching in professorship education, enhancing both instructional effectiveness and the overall learning experience.

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Publication Details

Journal
International Journal of Information System Modeling and Design
Published
2025-05-30
DOI
https://doi.org/10.4018/ijismd.378678
Citations
1
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
1.01
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article

Research on the Strategy of Enhancing Practice Teaching Effectiveness in Professorship Education Through CAD and Neural Network Technology

Yuemin Gao, Qiongyao Liu, Wei Wang, Wenqiang Dai et al.
1 citations
International Journal of Information System Modeling and Design
BIM and Construction Integration
1.01
article

Research on the Strategy of Enhancing Practice Teaching Effectiveness in Professorship Education Through CAD and Neural Network Technology

Yuemin Gao, Qiongyao Liu, Wei Wang, Wenqiang Dai, Guoxin Li
article en
1 citations

Abstract

This study explored the integration of computer-aided design (CAD) and neural network technology in vocational education, focusing on course design, skill training, and project-based learning. Through analysis, the study reveals how this technological integration improved students' practical skills, innovative thinking, and problem-solving abilities. Feedback from students indicated high satisfaction with the personalized resources provided by the system, which effectively met their learning needs. Empirical findings showed significant improvements in students' practical capabilities after applying CAD and neural network technology. Compared to traditional models, students demonstrated faster execution and better reporting skills. This integration marks a breakthrough in practical teaching in professorship education, enhancing both instructional effectiveness and the overall learning experience.

International Journal of Information System Modeling and DesignVol. 16(1)
Qinhuangdao Second Hospital (CN)
Openalex Percentile: Top 24%
BIM and Construction Integration
1.01
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