The effectiveness evaluation of industry education integration model for applied universities under back propagation neural network - Ying Qi; Wei Feng (Scientific Reports)
Abstract As the education field continues to advance, industry–education integration has become a crucial strategy for enhancing teaching quality in applied universities. This study investigates how artificial intelligence, specifically the back propagation neural network (BPNN), can be applied within an industry–education integration framework to strengthen students’ skills and employability. A series of experiments were conducted to assess the model’s effectiveness in linking theoretical learning with practical experience, as well as in improving students’ hands-on and innovative abilities. Results demonstrate that the BPNN-optimized model substantially boosts students’ overall competencies. For instance, the average academic score of students in the experimental group rose from 78.5 to 85.2, practical assessment scores increased from 76.8 to 88.4, and innovation scores improved from 74.2 to 82.5. Additionally, the employment rate for the experimental group reached 94%, surpassing the control group’s 76%, with significant gains in job satisfaction and career planning skills. These findings highlight that the BPNN-based industry–education integration model effectively strengthens students’ theoretical knowledge, practical skills, and employability, offering a valuable framework for enhanced university-industry collaboration.