The application of multivariate data chain network in the design of innovation and entrepreneurship teaching and learning in colleges and universities

Abstract

At present, although colleges and universities are actively exploring the construction of innovative and entrepreneurial teaching classrooms, they do not have the expected effect in practice. In this paper, based on the compressed perception technology, the complex data in the multivariate data chain network is sparsely represented, and multiple linear subsets in the data matrix are calculated by similarity. Statistical inference is used to generate the recommendation module after describing the encoding of innovation and entrepreneurship information packages in the data. Acquire the characteristics of students’ interest in innovation and entrepreneurial learning, create an interest graph module, and integrate the multi-perspective attention network to overcome the issue of recommendation bias. The analysis of teachers’ competence and students’ learning effectiveness involves the use of empirical testing methods. The results showed that among the 20 teachers, the teacher numbered 11 had a good performance with a competency of 0.8632 on entrepreneurship resources. The students’ 4 dimensions of innovation and entrepreneurship competence improved by more than 2 points, and the standard deviation was within the acceptable range. In the effect of the teaching model application, the mean value of students’ scores after improvement is more than 28. Teachers’ competence and students’ innovation and entrepreneurship ability are improved according to the model constructed in this paper.

Multi-Dimensional Data Analysis Platform (MuDAP): A Cognitive Science Data Toolbox

“…To validate the reliability of the data-dimension-reduction results, network analysis, a method that takes advantage of data symmetry is necessary [21][22][23]. Network analysis, especially in the fields of cognition and psychology [24][25][26], is a powerful tool for assessing the reliability of clustering results by leveraging the inherent symmetries within the data.…”