Dilemmas Facing College Labor Education in the Context of Deep Learning and the Way of Innovative Development

Abstract

Abstract Due to the influence of the background of the era and its ideological and cognitive factors, some college students lack independent labor ability and awareness, and it is necessary to build a labor education system that meets the requirements of the new era to further improve the effectiveness of educating people. By updating the pheromone, the ant colony algorithm is introduced to improve the convergence speed of the basic clustering algorithm. A linear combination of average similarity and cosine distance function is used to cluster labor education-related data, and the probability conversion function is used to increase the accuracy of clustering. The dilemmas that confront labor education in colleges and universities become clustered after a finite number of iterations. And quantitative analysis is carried out from the two aspects of learning quality of labor education in colleges and the influence of learner characteristics on learning effect to explore the innovative road of labor education in colleges and universities. The results show that: in the analysis of learning quality, learners’ average learning readiness is the highest (M=3.89, S=0.49), and the cluster centers corresponding to the sub-dimensions of “knowledge reserve”, “learning attitude” and “learning skills” are 3.66, 4.33 and 3.98. The mean value of “learning skills” is the highest, with 0.875 learners above the medium level, indicating that most of the learners think they can conduct independent learning in labor technology. The development of a labor education model is given an innovative direction by this study.