Analysis of the influencing factors of students' vocational ability based on joint improvement of convolutional neural network and multimodal data

Abstract

Students' professional ability includes innovation and entrepreneurship ability, independent ability, quality ability, professional ability and adaptability. The school's practical training teaching and professional setting, as well as the unique talent training model have a profound impact on training and development of students' professional ability. The continuous progress and development of society indicates that the competitiveness of occupational positions in the future will continue to increase. The talent market will also put forward newer and more difficult requirements for the quality of students. In study and life, cultivating students' good professional ability is the fundamental purpose of colleges and universities. Employers tend to pay more attention to overall quality of candidates when selecting employees. Whether students can enter the society smoothly after graduation has become an important task. It can be seen that it is very important to analyze the cultivation of students' professional ability and the influencing factors of professional ability. This work combines convolutional neural network (CNN) with the analysis of factors influencing students' vocational ability. First, this work proposes an improved residual block (IRB). It increases the width of the residual network by adding a residual connection channel to learn richer features. Second, this work proposes an improved attention module (IAM). It combines channel attention with spatial attention to improve model performance. Third, this work combines IRB and IAM to propose an SVAIFANet. The model can be applied to the analysis of factors influencing students' vocational ability. Fourth, this work conducts various experiments for proposed method, experimental data verify correctness for this method.