Visualization design of talent training paths in university education based on pattern knowledge

Abstract

In the background of big data informatization, social and economic development needs talent support, and colleges and universities are the main positions of talent training. Using the features of self-organization, distributed multi-intelligence and positive feedback of the ant colony algorithm, a personalized learning path recommendation algorithm supporting pattern knowledge is proposed by combining the above learning feature model. The subject starts an independent solution search simultaneously at multiple points in the problem space, which increases the algorithm’s reliability and gives the algorithm a strong global search capability. When performing talent development path recommendation, the collaborative mechanism of distributed multi-subjects of the ant colony algorithm makes it possible to find an acceptable path quickly. Combined with the new knowledge representation model, the matching degree of students and learning objects is calculated from three dimensions, the heuristic information is obtained accordingly, and the Euclidean distance is used for matching degree calculation. Using the ant colony optimization algorithm to analyze the paths for cultivating talents in college education, it is concluded that in terms of the number of teachers: the total number of teachers with positive senior titles in five colleges and universities is 238, accounting for 8.88%, and the number of full-time teachers with senior associate titles is 618, accounting for 23.07%, all of which are lower than the average level in Fujian Province. This study has some reference significance to the cultivation of applied talents in local colleges and universities and has some reference value to the government in formulating the policy of cultivating talents in colleges and universities.

Keywords

• big data informatization
• recommendation algorithm
• ant colony algorithm
• pattern knowledge
• matching degree
• 97b20